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 application filed on 11/27/2024. Claims 1-15 are presently pending and are presented for examination.
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.
Information Disclosure Statement
The information disclosure statements (IDS) were submitted on 11/27/2024. The submissions are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to because box 13-3-1 of Figure 1 appears to include a grammatically incorrect caption. Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
In addition to Replacement Sheets containing the corrected drawing figure(s), applicant is required to submit a marked-up copy of each Replacement Sheet including annotations indicating the changes made to the previous version. The marked-up copy must be clearly labeled as “Annotated Sheets” and must be presented in the amendment or remarks section that explains the change(s) to the drawings. See 37 CFR 1.121(d)(1). Failure to timely submit the proposed drawing and marked-up copy will result in the abandonment of the application.
Claim Objections
Claims 2, 6-8, and 11-12 are objected to because of the following informalities:
Claim 2 as currently presented states “…a seat part attribute…” to which the Examiner recommends updating to instead state “…[ [ a ] ] the seat part attribute…” so as to avoid potential misinterpretation.
Claim 2 as currently presented states “…a detected seat part…a detected seat part…” to which the Examiner recommends updating to instead state “…a detected seat part…[ [ a ] ] the detected seat part…”so as to avoid potential misinterpretation.
Claim 6 as currently presented states “…a neural network…” to which the Examiner recommends updating to instead state “…[ [ a ] ] the neural network…” so as to avoid potential misinterpretation.
Claim 7 as currently presented states “…a lookup table…” to which the Examiner recommends updating to instead state “…[ [ a ] ] the lookup table…” so as to avoid potential misinterpretation.
Claim 7 as currently presented states “…deriving the one or more seat parameters … using the detected one or more seat part attributes as parameters…” to which the Examiner recommends updating to instead state “…deriving the one or more seat parameters … using the detected one or more seat part attributes as inputs to the lookup table…” or the like, so as to avoid potential misinterpretation of the claim terminology.
Claim 8 as currently presented states “…the seat parts…” to which the Examiner recommends updating to instead state “…the one or more seat parts…” or similarly, “…the each detected seat parts…” so as to avoid potential misinterpretation.
Claim 8 as currently presented states “…the seat parameters…” to which the Examiner recommends updating to instead state “…the one or more seat parameters…” so as to avoid potential misinterpretation.
Claim 11 as currently presented states “…the seat…” to which the Examiner recommends updating to instead state “…the at least one seat…” so as to avoid potential misinterpretation.
Claim 11 as currently presented states “…the body parts…” to which the Examiner recommends updating to instead state “…the one or more body parts…” so as to avoid potential misinterpretation.
Claim 12 as currently presented states “…a body part attribute…” to which the Examiner recommends updating to instead state “…[ [ a ] ] the body part attribute…” so as to avoid potential misinterpretation.
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-8 and 13-15 are rejected under 35 U.S.C. 101, because the claimed invention is directed to an abstract idea without significantly more.
101 Analysis: Step 1
Independent claims 1, 13, 14, and 15 are directed towards a method, an apparatus, a system, and a non-transitory computer readable medium, respectively. Therefore, each of the independent claims 1, 13, 14, and 15 and the corresponding dependent claims 2-12 are directed to a statutory category of invention under Step 1.
101 Analysis: Step 2A, Prong 1
Regarding Prong 1 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
Independent claim 1 includes limitations that recite an abstract idea (emphasized below) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method of facilitating vehicle operation, 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 and, for each detected seat part, a seat part attribute; and
estimating one or more seat parameters of the at least one seat based on the detected one or more seat part attributes, the one or more seat parameters being indicative of a spatial configuration of the at least one seat in the vehicle cabin.
These limitations, as drafted, are a method that, under broadest reasonable interpretation, covers performance of the limitation as a mental concept. That is, nothing in the claim elements preclude the steps from practically being performed as a mental process. For example, “detecting…one or more seat parts…a seat part attribute…” may be interpreted as mentally detecting parts of a seat, and “estimating one or more seat parameters…” may be interpreted as mentally estimating seat configurations. Therefore, the claims are directed towards reciting an abstract idea.
101 Analysis: Step 2A, Prong 2
Regarding Prong 2 of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a "practical application.”
In the present case, the additional elements beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional elements” while the bolded portions continue to represent the “abstract idea”):
A method of facilitating vehicle operation, 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 and, for each detected seat part, a seat part attribute; and
estimating one or more seat parameters of the at least one seat based on the detected one or more seat part attributes, the one or more seat parameters being indicative of a spatial configuration of the at least one seat in the vehicle cabin.
For the following reason(s), the examiner submits that the above identified additional elements do not integrate the above-noted abstract idea into a practical application.
The limitation of “one or more images of at least part of a vehicle cabin” is directed towards insignificant extra-solution activity that is data gathering, which does not add any meaningful limits on the claim. Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application.
101 Analysis: Step 2B
Regarding Step 2B in the 2019 PEG, independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
The additional element of “one or more images of at least part of a vehicle cabin” is directed towards insignificant extra-solution data gathering. Examiner notes that receiving data from an image capturing device, as disclosed in claim 14, can alternatively be interpreted as being directed towards insignificant extra-solution data gathering depending on the structure of the supporting system.
Further, a conclusion that an additional element is insignificant extra-solution activity in Step 2A should be re-evaluated in Step 2B to determine if they are more than what is well understood, routine, conventional activity in the field. The additional limitation of “one or more images of at least part of a vehicle cabin” is a well-understood, routine, and conventional activity because the background recites that the image capturing device is a conventional camera within the vehicle. MPEP 2106.05(d)(II), and the cases cited therein, including Intellectual Ventures I, LLC v. Symantec Corp., 838 F.3d 1307, 1321 (Fed. Cir. 2016), TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610 (Fed. Cir. 2016), and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015), indicate that mere collection or receipt of data over a network is a well‐understood, routine, and conventional function when it is claimed in a merely generic manner. Hence, the claim is not patent eligible.
Dependent claims 2-8 and 13-15 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application.
Claim 2 further elaborates on the mental concept of detecting a seat part attribute, specifying that a portion of the detection is focused on.
Claims 3-7 elaborate on the mental concept of estimating seat parameters utilizing a generic component and recite additional mathematical concepts (e.g., specific algorithms) applied by generic component(s).
Claim 8 recites details pertaining to the generic components which are the focus of the abstract idea(s) and can also be characterized as a field of use.
Therefore, dependent claims 2-8 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Claim 13 recites analogous limitations to that of claim 1, deviating in the utilization of a memory storing instructions and a processor configured to execute the instructions (generic components), and is therefore rejected by the same premise.
Claim 14 recites analogous limitations to that of claim 1, deviating in the utilization of an image capturing device (generic component used in its ordinary capacity), and is therefore rejected by the same premise.
Claim 15 recites analogous limitations to that of claim 1, deviating in the utilization of a non-transitory computer-readable medium storing instructions (generic component applying the abstract idea), and is therefore rejected by the same premise.
Therefore, claims 1-8 and 13-15 are ineligible under 35 USC §101.
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 8-15 are rejected under 35 U.S.C. 103 as being unpatentable over Cho (US-2024/0029452) in view of Alcazar et al. (US-2012/0053794; hereinafter Alcazar).
Regarding claim 1, Cho discloses a method of facilitating vehicle operation (see Cho at least [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."); and
estimating 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 [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..." and [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.") …
However, while Cho discloses the estimation of seat parameters based on the detected one or more seat part attributes, the details of the seat parameters of Cho do not explicitly align with the instant claim.
Alcazar, in the same field of endeavor, teaches the following:
…the one or more seat parameters being indicative of a spatial configuration of the at least one seat in the vehicle cabin (see Alcazar at least [0040] “…At box 168, inverse kinematic calculations are performed to position the lower extremities, and define the fore-aft position of the driver seat 14...” [0044] “Next, the horizontal and vertical seat positions are defined in terms of the hip joint location and other factors. The horizontal seat position t.sub.n is normalized to a value between 0 and 1, where 0 is the fully forward position and 1 is the fully aft position. The vertical seat position d.sub.n is also normalized to a value between 0 and 1, where 0 is the fully downward position and 1 is the fully upward position…” and [0072]-[0073] "At box 172, a calculation of headrest elevation is made, such that the headrest 16 is positioned properly behind the driver's head. This calculation simply places the headrest 16 at an optimal location based on the sitting height of the driver 42. At box 174, a calculation is made to position the shoulder belt height adjuster 20 at the proper height. This is a simple calculation based on the seat vertical position and the driver's torso length t.sub.1... In summary, the process shown in the flow chart diagram 160 uses the driver's height, sitting height, and gender as input, estimates a complete set of anthropometric dimensions for the driver 42, and calculates optimal positions for all adjustable components in the vehicle 12.")…
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 method of facilitating vehicle operation as disclosed by Cho with seat parameters indicative of spatial configuration such as 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 a 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 a 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 8, Cho in view of Alcazar teach the method of claim 1 wherein:
the seat parts include one or more of a seat pad, a seat back rest (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..."), 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/or
the seat parameters include one or more of a seat track 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..." 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..."), a seat recline angle (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... 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 a seat head rest position.
Regarding claim 9, Cho in view of Alcazar teach the method of claim 1 wherein the one or more seat parameters are estimated based on a person sitting 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 method further 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
controlling vehicle equipment based on the estimated one or more seat parameters and the detected one or more body part attributes of the person (see Alcazar at least [0027] "Returning to discussion of the software system 40 of FIG. 2, the inverse kinematic calculation module 50 calculates positions of the driver seat 14, outside rearview mirrors 18, pedals 24, steering wheel and column 22, and other components which provide optimum comfort and safety for the driver 42. These calculations are based on the anthropometric model data, AM1-AM11, and the vehicle data, V1-V19, as discussed above. The details of the calculations performed in the inverse kinematic calculation module 50 will be provided below. Finally in the software system 40, the outputs of the inverse kinematic calculation module 50 are provided to an adjustment command module 52, which commands each of the adjustable components to move to the position and orientation computed by the inverse kinematic calculation module 50." and [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.").
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 and 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 controlling the vehicle equipment includes one or more of:
adjusting a setting or a configuration of the vehicle equipment (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 vehicle equipment.
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 control of vehicle equipment 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 vehicle equipment includes one or more of the seat (see Alcazar at least [0077] "At box 296, the driver 42 re-adjusts the driver seat 14 during driving..."), a seat part of the seat (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."), a controller (see Alcazar at least [0018] “...A control module 28 controls the operation of the driver convenience system 10, including computing optimal positions and orientations for each of the components 14-24, and commanding the adjustment of each of the components 14-24 to its optimal position and orientation...”), an airbag, and an acoustic and/or visual output device, and/or
the 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...").
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 control of vehicle equipment such as taught by Cho in view of Alcazar with the specificity of the vehicle equipment as further taught by Alcazar with a reasonable expectation of success for reasons similar to those provided above in claim 9.
Regarding claim 12, Cho in view of Alcazar teach the method of claim 9 wherein a 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: an image capturing device configured to capture one or 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).
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 parameters (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).
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. Shaik et al. (US-2023/0013133) teaches the detection of vehicle cabin information such as a seating zone and seating zone occupancy.
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/S.P.R./Examiner, Art Unit 3663
/ABBY J FLYNN/Supervisory Patent Examiner, Art Unit 3663