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 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.
Status of Claims
This Office Action is in response to the Applicant’s Response dated 7/9/2026. Claims 1-7 and 9-15 are presently pending and are presented for examination.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 4/9/2026. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. All pending claims therefore have an effective filing date of 12/6/2023.
Response to Amendment
Applicant’s amendments, see pages 8-9 of 12, filed 7/9/2026, with respect to drawing objections, claim objections, and 101 rejections have been fully considered and are persuasive. The drawing objections, claim objections, and 101 rejections of record have been withdrawn.
Response to Arguments
Applicant's first argument, see pages 9-10 of 12, filed 7/9/2026, has been fully considered but is not persuasive. The Applicant has argued that Cho does not explicitly disclose or teach the detection of an individual seat parts, however the Examiner respectfully disagrees. Cho describes the detection of an entire seat as well as a seat belt from an obtained image, which reads on the Markush group as claimed "...detecting, from one or more images of at least part of a vehicle cabin, one or more seat parts...wherein the one or more seat parts include one or more of a seat pad, a seat back rest, a seat head rest, and a seat buckle...".
Applicant's second argument, see pages 10-11 of 12, filed 7/9/2026, has been fully considered but is not persuasive. The Applicant has argued that Cho does not explicitly disclose or teach the estimation of seat parameters from images, however the Examiner respectfully disagrees. Cited paragraph [0105] states “…considering the position of the seat 110 when determining the area of the seat in the captured images…” of which the estimation pertains to the considered position, such that both the seat track position and seat recline angle are considered.
Applicant's third argument, see page 11 of 12, filed 7/9/2026, has been fully considered but is not persuasive. The Applicant has argued that neither Cho nor Alcazar explicitly disclose or teach the adjustment of a seat based on estimated seat parameters, however the Examiner respectfully disagrees. While Cho discloses the estimation of seat parameters, the detections are merely for the sake of monitoring; whereas secondary reference Alcazar teaches the adjustment of a vehicle seat according to conditions withing the vehicle.
A detailed rejection follows 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, 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-4 and 9-15 are rejected under 35 U.S.C. 103 as being unpatentable over Cho (US-2024/0029452; already of record) in view of Alcazar et al. (US-2012/0053794; hereinafter Alcazar; already of record).
Regarding claim 1, Cho discloses a computer-implemented method of facilitating vehicle operation (see Cho at least Abs and [0002]), the method comprising:
detecting, from one or more images of at least part of a vehicle cabin, one or more seat parts of at least one seat in the vehicle cabin (see Cho at least [0104] "...The images captured by the camera 140 may include at least one seat 110 or a seated occupant along with the seat 110. Furthermore, it is appropriate for the camera 140 to be equipped in the front of the vehicle's interior to face the rear, allowing for a clear representation of the front of the seat 110 and the seated occupant…") and, for each detected seat part, a seat part attribute (see Cho at least [0066] "…In this case, the first bounding box 210 corresponds to the upper body area of the seated occupant in the acquired image, while the second bounding box 220 encompasses the detected seat belt 10 from the acquired image. The detailed description of how the controller 150 determines whether the seated occupant is wearing the seat belt 10 is provided with reference to FIGS. 2 and 3."), wherein the one or more seat parts include one or more of a seat pad, a seat back rest, a seat head rest, and a seat buckle (see Cho at least [0076] "By analyzing the images obtained through the camera 140, as described in FIG. 2, it is possible to determine that the seat belt 10 is not worn, even if the buckle sensors indicate that the seat belt is worn, when the seated occupant is not actually wearing the seat belt 10..." and [0104] "...The images captured by the camera 140 may include at least one seat 110 or a seated occupant along with the seat 110…");
estimating, from the one or more images, one or more seat parameters of the at least one seat based on the detected one or more seat part attributes (see Cho at least [0105] "The images captured by the camera 140 may include the seat 110 and the seated occupant, and when the seat 110 undergoes changes in position, such as moving forward or backward or reclining, while the camera 140 remains fixed, there will be noticeable variations in the area occupied by the seat 110 within the captured images. Even though the changes in position do not affect the actual area of the seat 110 itself, when the seat 110 is positioned in the front compared to the rear, the seat 100 will be closer to the camera 140, resulting in a larger area occupied in the captured images. Furthermore, when the seat back has a smaller recline angle, it will occupy a larger area in the captured images compared to when the seat back has a larger recline angle. Therefore, considering the position of the seat 110 when determining the area of the seat in the captured images may lead to more accurate results."), wherein:
the one or more seat parameters include one or more of a seat track position, a seat recline angle, and a seat head rest position (see Cho at least [0101] "...Meanwhile, in an embodiment of the present disclosure, the seat 110 may be adjustable in position. The seat's position may include at least one of the position in the driving direction and a direction parallel to the driving direction of the seat 110 arranged on the driver's seat 111, front passenger seat 112, and the seat back angle. Here, seat's position may refer to the state of the seat 110, which includes the location of the seat 110 within a certain range achieved by moving the seat forward and backward via a mechanism such as seat tracks. In addition, seat back angle may refer to the angle of the seat back in relation to the seating surface or the ground, varying in the recline or relax mode of the seat 110." and [0105] "The images captured by the camera 140 may include the seat 110 and the seated occupant, and when the seat 110 undergoes changes in position, such as moving forward or backward or reclining, while the camera 140 remains fixed, there will be noticeable variations in the area occupied by the seat 110 within the captured images.”), and
the one or more seat parameters indicate a spatial configuration of the at least one seat in the vehicle cabin (see Cho at least [0105] "The images captured by the camera 140 may include the seat 110 and the seated occupant, and when the seat 110 undergoes changes in position, such as moving forward or backward or reclining, while the camera 140 remains fixed, there will be noticeable variations in the area occupied by the seat 110 within the captured images..."); and
…
…the estimated one or more seat parameters (see Cho at least [0105] "…Therefore, considering the position of the seat 110 when determining the area of the seat in the captured images may lead to more accurate results.")…
…the estimated one or more seat parameters (see Cho at least [0105] "…Therefore, considering the position of the seat 110 when determining the area of the seat in the captured images may lead to more accurate results.").
However, while Cho discloses the analysis of seat parameters, Cho does not explicitly disclose or teach the following:
…at least one of:
…adjusting the at least one seat…
…adapting deployment of an airbag…
Alcazar, in the same field of endeavor, teaches the following:
…at least one of:
…adjusting the at least one seat (see Alcazar at least [0077] "At box 296, the driver 42 re-adjusts the driver seat 14 during driving. At box 298, the driver convenience system 10 re-adjusts the outside rearview mirrors 18 and the headrest 16 based on the new seating position of the driver 42, and using the calculations described above for the process of the flow chart diagram 160.")…
…adapting deployment of an airbag…
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 detection of body attributes as disclosed by Cho with the control of vehicle equipment such as further taught by Alcazar with a reasonable expectation of success for the sake of ensuring optimal positions and orientations for adjustable components (see Alcazar at least [0002]).
Regarding claim 2, Cho in view of Alcazar teach the method of claim 1 wherein the seat part attribute includes:
a bounding box of a detected seat part (see Cho at least [0066] "…In this case, the first bounding box 210 corresponds to the upper body area of the seated occupant in the acquired image, while the second bounding box 220 encompasses the detected seat belt 10 from the acquired image..."), wherein the bounding box surrounds the detected seat part in the one or more images (see Cho at least [0066] "…In this case, the first bounding box 210 corresponds to the upper body area of the seated occupant in the acquired image, while the second bounding box 220 encompasses the detected seat belt 10 from the acquired image..."), and/or
a specific point or location of the detected seat part in the one or more images.
Regarding claim 3, Cho in view of Alcazar teach the method of claim 1 wherein the one or more seat parameters are estimated using geometric modeling (see Alcazar at least [0047] "Equations (3)-(7) above define the basic framework of fore-aft and vertical positions of the hip joint and seat, in terms of the angles p.sub.3 and p.sub.6 and other variables. Inverse kinematics can now be used to compute the internal angles, including p.sub.3 and p.sub.6, in the geometric model 200 of FIG. 6. Using inverse kinematics to solve for p.sub.3 and p.sub.6 will allow for the calculation of the seat and lower body positions.") or a neural network or a lookup table.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the estimation of seat parameters as disclosed by Cho with the usage of a geometric model such as further taught by Alcazar with a reasonable expectation of success for the sake of producing accurate estimations (see Alcazar at least [0007]).
Regarding claim 4, Cho in view of Alcazar teach the method of claim 3 wherein estimating the one or more seat parameters using geometric modeling includes:
determining geometric features of at least part of the detected one or more seat part attributes and/or a relationship between one or more groups of at least two detected seat part attributes (see Alcazar at least [0064] "The above calculations performed at the box 168, including Equations (1)-(32), fully resolve the geometric model 200. This defines the location of the hip joint 130, the ankle, knee, and hip angles, the fore-aft and vertical positions of the driver seat 14, and the tilt angles of the seat cushion 108 and the seat back 110..."), and
calculating the one or more seat parameters based on the determined geometric features (see Alcazar at least [0064] "The above calculations performed at the box 168, including Equations (1)-(32), fully resolve the geometric model 200. This defines the location of the hip joint 130, the ankle, knee, and hip angles, the fore-aft and vertical positions of the driver seat 14, and the tilt angles of the seat cushion 108 and the seat back 110...").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the estimation of seat parameters as disclosed by Cho with the usage of a geometric model such as further taught by Alcazar with a reasonable expectation of success for reasons similar to those provided above in claim 3.
Regarding claim 9, Cho in view of Alcazar teach the method of claim 1 wherein:
in response to a person sitting on the at least one seat in the vehicle cabin, the method includes detecting, from the one or more images, one or more body parts of the person and, for each body part, a body part attribute (see Cho at least Fig 2 and [0069]-[0070] "First, a description is made of the process of determining the first bounding box 210 corresponding to the upper body region of the seated occupant from the acquired image. The controller 150 may detect at least one preset point from the acquired image, which is included in the upper body region of the seated occupant, to determine the first bounding box 210. In this case, each point may be preset to correspond to predetermined locations on the upper body of the seated occupant in the image, such as the left shoulder 211, right shoulder 212, and abdominal center 213 in order to ensure that the first bounding box 210 captures the upper body region of the seated occupant. The controller 150 may determine the first bounding box 210 based on the detected points..."), and
the adjusting of the at least one seat and/or the adapting of the deployment of the airbag is further based on the detected one or more body part attributes of the person (see Alcazar at least [0077] "…At box 298, the driver convenience system 10 re-adjusts the outside rearview mirrors 18 and the headrest 16 based on the new seating position of the driver 42, and using the calculations described above for the process of the flow chart diagram 160.").
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 detection of body attributes as disclosed by Cho with the control of vehicle equipment such as further taught by Alcazar with a reasonable expectation of success for the sake of ensuring optimal positions and orientations for adjustable components (see Alcazar at least [0002]).
Regarding claim 10, Cho in view of Alcazar teach the method of claim 9 wherein the adjusting of the at least one seat and/or the adapting of the deployment of the airbag includes one or more of:
adjusting a setting or a configuration of the at least one seat and/or the airbag (see Alcazar at least [0077] "At box 296, the driver 42 re-adjusts the driver seat 14 during driving. At box 298, the driver convenience system 10 re-adjusts the outside rearview mirrors 18 and the headrest 16 based on the new seating position of the driver 42, and using the calculations described above for the process of the flow chart diagram 160."), and
causing a predetermined operation of the at least one seat and/or the airbag.
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 at least one seat such as taught by Cho in view of Alcazar with the adjustment of settings such as further taught by Alcazar with a reasonable expectation of success for reasons similar to those provided above in claim 9.
Regarding claim 11, Cho in view of Alcazar teach the method of claim 9 wherein:
the one or more body parts of the person include one or more of a head and a torso of the person (see Cho at least Fig 2 and [0069]-[0070] "First, a description is made of the process of determining the first bounding box 210 corresponding to the upper body region of the seated occupant from the acquired image. The controller 150 may detect at least one preset point from the acquired image, which is included in the upper body region of the seated occupant, to determine the first bounding box 210. In this case, each point may be preset to correspond to predetermined locations on the upper body of the seated occupant in the image, such as the left shoulder 211, right shoulder 212, and abdominal center 213 in order to ensure that the first bounding box 210 captures the upper body region of the seated occupant. The controller 150 may determine the first bounding box 210 based on the detected points...").
Regarding claim 12, Cho in view of Alcazar teach the method of claim 9 wherein the body part attribute includes one or more of:
a bounding box of a detected body part, wherein the bounding box surrounds the detected body part in the one or more images (see Cho at least [0089] "...In this case, each point may be preset to correspond to predetermined locations on the upper body of the seated occupant in the image, such as the left shoulder 211, right shoulder 212, and abdominal center 213 in order to ensure that the first bounding box 210 captures the upper body region of the seated occupant."),
one or more keypoints of the detected body part, wherein the one or more keypoints define characteristic points of the detected body part in the one or more images (see Cho at least [0094] "...Human pose estimation is the process of detecting multiple key points corresponding to important body parts of a person and using them to estimate the pose of the subject, and in an embodiment of the present disclosure, the detected key points belonging to the upper body of the seated occupant may be used as points for determining the first bounding box 210..."), and
an orientation of the detected body part, wherein the orientation defines a spatial direction of the detected body part in the vehicle cabin.
Regarding claim 13, Cho in view of Alcazar teach the analogous material of that in claim 1 as recited in the instant claim and is rejected for similar reasons. Additionally, Cho discloses
…an apparatus (see Cho at least Abs)…
…memory hardware configured to store instructions (see Cho at least [0060])…
…processor hardware configured to execute the instructions (see Cho at least [0060])…
Regarding claim 14, Cho in view of Alcazar teach the analogous material of that in claim 1 as recited in the instant claim and is rejected for similar reasons. Additionally, Cho discloses the following:
…a system (see Cho at least [0060]) comprising: the apparatus of claim 13; and an image capturing device configured to capture one or more images of at least part of a vehicle cabin (see Cho at least [0064]; camera 140), including at least one seat in the vehicle cabin…
Regarding claim 15, Cho in view of Alcazar teach the analogous material of that in claim 1 as recited in the instant claim and is rejected for similar reasons.
Claims 5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Cho in view of Alcazar, and further in view of Luo et al. (US-2006/0140446; hereinafter Luo; already of record).
Regarding claim 5, Cho in view of Alcazar teach the method of claim 4 wherein:
the geometric features include one or more of a position of a seat part attribute (see Alcazar at least [0064] "The above calculations performed at the box 168, including Equations (1)-(32), fully resolve the geometric model 200. This defines the location of the hip joint 130, the ankle, knee, and hip angles, the fore-aft and vertical positions of the driver seat 14, and the tilt angles of the seat cushion 108 and the seat back 110..."), a size of a seat part attribute, a shape of a seat part attribute, a relative two-dimensional, horizontal and/or vertical position between two or more seat part attributes, and/or
…
However, while Cho discloses the calculation of seat parameters, and Alcazar teaches the use of a geometric feature, neither Cho nor Alcazar explicitly disclose or teach the following:
…calculating the one or more seat parameters includes using a linear least-squares method or a Levenberg-Marquart algorithm.
Luo, in the same field of endeavor, teaches the following:
…calculating the one or more seat parameters includes using a linear least-squares method (see Luo at least [0043] "The selected models are provided to an image editor 170 that eliminates the seat back and head rest from the stereo disparity map. In the illustrated example, a best-fit line can be determined for the rearward edge of the seat back according to a least squares analysis of the pixels within the contour image matching pixels within the contour model...") or a Levenberg-Marquart algorithm.
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 determination of seat parameters such as disclosed by Cho with the use of a linear least-squares method such as taught by Luo with a reasonable expectation of success for the sake of obtaining repeatable classifications of seat parameters (see Luo at least [0030]-[0031]).
Regarding claim 7, Cho in view of Alcazar teach the method of claim 3 wherein estimating the one or more seat parameters … includes:
deriving the one or more seat parameters … using the detected one or more seat part attributes as an input to the lookup table (see Cho at least [0077] "...On the other hand, according to the embodiments of the present disclosure, by utilizing the first bounding box 210 and the second bounding box 220, it becomes possible to determine the use of the seat belt 10 based solely on the information of the four corner points of the bounding boxes and the width and height information...").
However, while Cho derives seat parameters using seat part attributes, neither Cho nor Alcazar detail the use of a lookup table.
Luo, in the same field of endeavor, teaches the following:
…deriving the one or more seat parameters from the lookup table (see Luo at least [0036] "Once a contour image has been generated, it is provided to a model selector 166 that attempts to match one of a plurality of three-dimensional seat contour models 168 to at least a portion of the generated contour image. Each of the seat contour models 168 represents a three-dimensional representation of a rearward contour of the seat in a potential positioning along one or more ranges of motion..." and [0039] "In a generalized Hough transform, a feature or contour having a known shape and orientation can be located within an image. The generalized Hough transform can be utilized to located features that cannot be described by a simple analytical equation. First, an arbitrary reference point within the image can be selected, from which the shape of the feature can be defined according to a series of normal lines between the points in the line and the reference point. The parameters for each line (e.g., the slope and normal distance from the reference point) can be inputted into a lookup table representing the contour model.")…
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 seat parameter derivation as disclosed by Cho with a lookup table such as taught by Luo with a reasonable expectation of success for the sake of obtaining repeatable classifications of seat parameters (see Luo at least [0030]-[0031]).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Cho in view of Alcazar, and further in view of Zeng et al. (US-2023/0020385; hereinafter Zeng; already of record).
Regarding claim 6, Cho in view of Alcazar teach the method of claim 3. However, while Cho discloses the estimation of seat parameters, and Alcazar teaches the use of a geometric model to aid in seat parameter estimation, neither reference explicitly discloses or teaches the following:
…inputting the detected one or more seat part attributes into the neural network…
…receiving the one or more seat parameters as output of the neural network…
Zeng, in the same field of endeavor, teaches the following:
…inputting the detected one or more seat part attributes into the neural network (see Zeng at least Fig 7 and [0079] "The grid classification branch network 620 is described below in connection with FIG. 6 to FIG. 8. The grid classification branch network of the present disclosure is a deep learning neural network for identifying the safety belt based on image classification. Using the grid classification branch network according to the disclosure for safety belt detection can be understood as looking for a set of safety belt positions in some rows or columns of the image, that is, position selection and classification based on the direction of rows or columns. In an embodiment, obtaining the grid classification diagram 602 output based on image classification from the grid classification branch network 620 includes the following steps S720-S780.")…
…receiving the one or more seat parameters as output of the neural network (see Zeng at least Fig 7 and [0079] "The grid classification branch network 620 is described below in connection with FIG. 6 to FIG. 8. The grid classification branch network of the present disclosure is a deep learning neural network for identifying the safety belt based on image classification. Using the grid classification branch network according to the disclosure for safety belt detection can be understood as looking for a set of safety belt positions in some rows or columns of the image, that is, position selection and classification based on the direction of rows or columns. In an embodiment, obtaining the grid classification diagram 602 output based on image classification from the grid classification branch network 620 includes the following steps S720-S780.")…
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 determination of seat parameters such as disclosed by Cho with the use of a neural network such as taught by Zeng with a reasonable expectation of success so as to ensure accurate detections (see Zeng at least [0024]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Muralidharan (US-11,541,794) teaches the detection of a headrest with respect to a passenger's head, and provides adjustments to the headrest accordingly.
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 SEAN REIDY whose telephone number is (571) 272-7660. The examiner can normally be reached on M-F 7:00 AM- 3:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Flynn can be reached on (571) 272-9855. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/S.P.R./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663
