DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
2. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/20/2026 has been entered.
3. In the applicant’s submission, claims 1, 18, and 20 were amended. Accordingly, claims 1-9 and 11-20 are pending and being examined. Claims 1, 18, and 20 are independent form.
Claim Rejections - 35 USC § 103
4. 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.
5. Claims 1-4, 11, 13, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sakakida et al (US Pub 2010/0182425, hereinafter “Sakakida”) in view of Ahmed et al (JP2003025953, hereinafter “Ahmed”).
Regarding claim 1, Sakakida discloses a method (the vehicle interior state recognition device/method shown by fig.8) comprising:
receiving one or more current images of a portion of an interior of a vehicle showing at least one of at least a part of a passenger seated on a vehicle seat or at least a part of the vehicle seat, at least one of the one or more current images showing at least the part of the passenger seated on the vehicle seat (see S6 “pick up images” of fig.8 and para.78: “The images picked up by the pick-up means 16 include at least respective images of the driver's seat 2 and its passenger A and the passenger's seat 3 and its passenger B as shown in FIG. 6.”);
determining, based on the one or more current images: a number of body keypoints indicative of locations of defined body portions of the passenger; and a number of seat keypoints indicative of locations of defined points of the vehicle seat; estimating, based at least on a correlation of the determined body keypoints with the determined seat keypoints, a height of the passenger seated on the vehicle seat (see para.101, lines 8-11: “In any case the body size of the passenger's seat passenger B can be determined accurately by comparing the contour of the passenger's seat 3 with the contour of the passenger's seat passenger B on the image picked up”; wherein “the body size of the passenger's seat passenger B is determined by comparing a passenger's sitting height H with the recognizing member 32 which is provided at the seatback 3a of the passenger's seat 3 based on the picked up images (FIG. 6) read in the step S7”; see para.79, lines 3-7.);
see para.80: “the body size of the passenger's seat passenger B [estimated at step S8 and based on the passenger's sitting height H] are used in the inflation control of the airbag device 24 by the airbag control means 19. For example, when the sitting height of the passenger's seat passenger B is shorter than a specified height, so it is determined that the passenger B is like a child or a relatively short adult who belongs to a 5% shortest American Female group (AF05), the inflation of the airbag for the passenger' seat is prohibited or restrained even if the vehicle crash occurs.”);
Sakakida does not explicitly disclose “determining a height estimation confidence value indicating a confidence of the estimated height of the passenger compared to previous estimations of the height of the passenger; comparing the confidence value with a given threshold; and outputting the estimated height to the seat occupancy classification system in response to determining that the confidence value is greater than or equal to the given threshold; and increasing the threshold in response to determining that the confidence value is greater than or equal to the threshold” recited by the claim.
However, in the same field of endeavor, that is, in the field of classifying the occupant on a seat for unfolding an air bag, Ahmed (see JP2003025953-Eng, pg.10, line 30---pg.11, line 36; and/or see JP2003025953, para.47-para.50, and fig.5) teaches: determining the boundary values (i.e., the confidence values) for classifying an occupant/passenger on a seat of a vehicle. For example, if the occupant’ HLOC is larger than 110, the occupant’ AREA is larger than 1,000, and the occupant’ HEIGHT is larger than 100, then the occupant is an ADULT. On the other hand, if the occupant’ VLTR is among 35-115, the occupant’ AREA is larger than 1,000, and the occupant’ height is among 50-100, then the occupant is an ADULT. In other words, Ahmed does not only teach threshold values to identify adult but also teach they are vary based on other features. See JP2003025953-Eng, pg.11, lines 13---32. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Ahmed into the teachings of Sakakida and set vary height threshold values for presenting different types of passengers. Suggestion or motivation for doing so would have been to “automatically “classify the occupant on a seat for unfolding an air bag”” as taught by Ahmed, see Abstract. Therefore, the claim is unpatentable over Sakakida in view of Ahmed.
Regarding claim 2, the combination of Sakakida and Ahmed discloses the method of claim 1, wherein determining the number of seat keypoints comprises: determining the number of seat keypoints based on at least one of the one or more current images showing an unoccupied vehicle seat before the passenger has occupied the vehicle seat (Sakakida, see para.101, lines 8-11: “In any case the body size of the passenger's seat passenger B can be determined accurately by comparing the contour of the passenger's seat 3 with the contour of the passenger's seat passenger B on the image picked up”).
Regarding claims 3, 4, in embodiment 1, Sakakida does not explicitly disclose, determining one or more seat keypoints covered by the passenger seated on the vehicle seat. However, in embodiment 2, see para.109, Sakakida teaches calculating “the heights of the headrests 51-56 of the seats 2-6 based on the images (FIG. 14) read in the step S107 (step S108)”. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to modify embodiment 1 of Sakakida based on embodiment 2 of Sakakida and further determine the height of the headrest covered by the passenger seated on the vehicle seat based on the images. Suggestion or motivation for doing so would have been to improve “the safety at the vehicle crash (the rear crash, in particular)” as taught by Sakakida, cf., Par.117, 8-12. Therefore, the claim is unpatentable over the combination of Sakakida and Ahmed.
Regarding claim 11, the combination of Sakakida and Ahmed discloses the method of any one of claim 1, wherein estimating the height of the passenger seated on the vehicle seat is further based on at least one of: a position classification of the passenger seated on the vehicle seat; or a face identifier associated with a face specified by at least one face reference image of the passenger (Sakakida, see para.80: “ For example, when the sitting height of the passenger's seat passenger B is shorter than a specified height, so it is determined that the passenger B is like a child or a relatively short adult who belongs to a 5% shortest American Female group (AF05), the inflation of the airbag for the passenger' seat is prohibited or restrained even if the vehicle crash occurs. Thereby, the airbag for the passenger' seat is inflated at a normally high speed only for an adult having the normally large body size, so that the safety of the passenger can be ensured properly.”).
Regarding claim 13, the combination of Sakakida and Ahmed discloses the method of claim 1, wherein the defined points of the vehicle seat comprise at least one of: bottom left of a backrest of the vehicle seat, bottom right of the backrest of the vehicle seat, top left of the backrest of the vehicle seat, top right of the backrest of the vehicle seat, top left of a headrest of the vehicle seat, or top right of the headrest of the vehicle seat (Sakakida, see, e.g., seatbacks 2a and 3a in fig.1 and para.61).
Regarding claims 18-20, each of which is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
6. Claims 5-9, 12, 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over Sakakida in view of Ahmed and further in view of Friedman et al (WO 2021/005603, hereinafter “Friedman”).
Regarding claim 5, the combination of Sakakida and Ahmed does not explicitly disclose, “determining a seat keypoint confidence value indicating a confidence of the seat keypoints determined based on the one or more current images” as recited in the claim. However, in same field of endeavor, Friedman, see para.147, teaches that “the annotation data store 234 stores the original 2D images and the related superposed skeleton for each object [including the driver seat and driver, see para.151]. According to one embodiment, the annotation data store 234 may further store related data for each pixel or key-point at the skeleton such as one or more confidence grades.” In other words, Friedman teaches that, the annotation data store 234 stores: (1) original 2D images of a vehicle interior cabin including the driver sitting in the driver seat and passenger sitting in the passenger seat; (2) the related superposed skeleton for each of the driver, driver seat, passenger, and passenger seat in each image; and (3) the related data for each key-point at the skeleton as confidence grades, wherein a confidence grade states “how confident, relevant and accurate the location of the generated key-point of the skeleton is” (see para.147). It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Friedman into the teachings of the combination of Sakakida and Ahmed and further measure the confidence grade for each key-point at each skeleton of each object in images of a vehicle interior cabin taught by Friedman. Suggestion or motivation for doing so would have been to “estimat[e] the mass of one or more occupants in a vehicle cabin comprising obtaining multiple images of the one or more occupants” as taught by Friedman, see Abstract. Therefore, the claim is unpatentable over Sakakida in view of Ahmed and further in view of Friedman.
Regarding claim 6, the combination of Sakakida, Ahmed and Friedman discloses the method of claim 5, wherein determining the number of seat keypoints further comprises: resetting a seat keypoint confidence threshold if a movement of the vehicle seat has been detected; and maintaining the seat keypoint confidence threshold if a movement of the vehicle seat has not been detected (Friedman, see para.167: “Figure 5A shows a captured image 500 of a passenger 512 seating on a passenger front seat of a vehicle which will be filtered out based on the predefined filtering criteria, such as due to the short measured torso. Specifically, as illustrated in image 500, the passenger 512 is leaning forward with respect to an imaging sensor, and accordingly, the measured passenger's torso length 516 (e.g., the length between the neck and the pelvis measured between the skeleton points 511 and 513) of the skeleton 518 is short relative to the shoulders’ width.” In other words, the images for measuring the confidence grade for each key point of each object are filtered out based on the predefined filtering criteria. When the captured image includes an object which is moved and out of a standard position, such as the passenger leaning forward with respect to an imaging sensor, the captured image will be filtered out from the images for measuring the confidence grades for the key points of the object. Thus, the claimed invention is an obvious variation of Friedman.).
Regarding claim 7, the combination of Sakakida, Ahmed and Friedman discloses the method of claim 6, wherein determining the number of seat keypoints further comprises: comparing the seat keypoint confidence value with the seat keypoint confidence threshold; and in response to determining that the seat keypoint confidence value is equal to or above the seat keypoint confidence threshold, increasing the seat keypoint confidence threshold and replacing seat keypoints determined based on one or more previous images by the determined seat keypoints based on the one or more current images (ibid. In other words, the method of Friedman will store those image whose confidence values are higher than the predefined filtering criteria (such as the visual 2D image shown by fig.6) while discard those image whose confidence values are lower than the predefined filtering criteria (such as the image shown by fig.5A).
Regarding claim 8, the combination of Sakakida, Ahmed and Friedman discloses the method of claim 6, wherein determining the number of seat keypoints further comprises: comparing the seat keypoint confidence value with the seat keypoint confidence threshold; and in response to determining that the seat keypoint confidence valueis below the seat keypoint confidence threshold, discarding the determined seat keypoints based on the one or more current images and maintaining the seat keypoints determined based onone or more previous images (see the explanation in the rejection of claim 7.).
Regarding claim 9, the combination of Sakakida, Ahmed and Friedman discloses the method of any one of claim 5, wherein estimating the height of the passenger seated on the vehicle seat is further based on at least one of: a position classification of the passenger seated on the vehicle seat; or a face identifier associated with a face specified by at least one face reference image of the passenger (Sakakida , face recognition, see figs.9A—9C; Friedman, see para.87: “the system 100 may further include a face detector sensor and/or face detection and/or face recognition software module for analyzing the captured 2D and/or 3D images.”).
Regarding claim 12, the combination of Sakakida, Ahmed and Friedman discloses the method of claim 9, further comprising: determining a height estimation confidence value indicating a confidence of the estimated height of the passenger compared to previous estimations of the height of the passenger; comparing the confidence value with a given threshold; and outputting the estimated height to the seat occupancy classification system in response to determining that the confidence value is equal to or above the threshold (Sakakida, see para.80: “For example, when the sitting height of the passenger's seat passenger B is shorter than a specified height, so it is determined that the passenger B is like a child or a relatively short adult who belongs to a 5% shortest American Female group (AF05), the inflation of the airbag for the passenger' seat is prohibited or restrained even if the vehicle crash occurs. Thereby, the airbag for the passenger' seat is inflated at a normally high speed only for an adult having the normally large body size, so that the safety of the passenger can be ensured properly.”).
Regarding claim 14, the combination of Sakakida, Ahmed and Friedman discloses the method of claim 1, wherein determining the number of body keypoints and the number of seat keypoints comprises: transforming location indications of the body keypoints and the seat keypoints from a coordination system given by the one or more current images to a normalized coordination system (Friedman, see the depth images including the driver and driver seat are displayed in the normalized 3D camera system and shown in fig.3A and fig.3B, see para.131: “where each [point] color represents the distance of the spot from a reference point (e.g. camera). For example, the scale 282 includes a grayscale color for a distance of around 40 cm from the camera and continuously the color representation changes to black scale for a distance around 140 cm from the camera and so on the color scale varies according to the distance. Accordingly, the multiple patterns spot 281 of the captured image 285 shown in Figure 3B are analyzed to yield a depth representation image 287 of the scene as illustrated in Figure 3A.”).
Regarding claim 15, the combination of Sakakida, Ahmed and Friedman discloses the method of claim 14, wherein the normalized coordination system is defined by reference points in the interior of the vehicle shown on the one or more current images (Friedman, see the depth images including the driver and driver seat are displayed in the normalized 3D camera system and shown in fig.3A and fig.3B, see para.131: “where each [point] color represents the distance of the spot from a reference point (e.g. camera). For example, the scale 282 includes a grayscale color for a distance of around 40 cm from the camera and continuously the color representation changes to black scale for a distance around 140 cm from the camera and so on the color scale varies according to the distance. Accordingly, the multiple patterns spot 281 of the captured image 285 shown in Figure 3B are analyzed to yield a depth representation image 287 of the scene as illustrated in Figure 3A.”).
Regarding claim 16, the combination of Sakakida, Ahmed and Friedman discloses the method of claim 15, wherein the reference points comprise: at least one vertical normalization reference point for normalizing vertical positions of the body keypoints and the seat keypoints, and at least two horizontal normalization reference points for normalizing horizontal positions of the body keypoints and the seat keypoints and for scaling coordinates of body keypoints and seat keypoints (Friedman, see the scaled vertical and scaled horizontal coordinates in the images shown by Figure 3A and Figure 3B).
Regarding claim 17 the combination of Sakakida, Ahmed and Friedman discloses the method of claim 16, wherein at least one of:the at least one vertical normalization reference point is given by a top of a backrest of the vehicle seat, or at least two horizontal normalization reference points are given by a location of two seat belt pillar loops of the vehicle (Friedman, wherein the images shown in Figure 3A and Figure 3B are images of a vehicle cabin including the driver, driver seat, passenger, and passenger seat as shown by fig.1; wherein the head of each passenger seated in the seat with the seatbelt shown by fig.1 is located in the vertical (y) direction).
Response to Arguments
7 Applicant’s arguments, filed on 01/06/2026, have been fully considered but they are not persuasive.
On page 12, the applicant argues Ahmed’s threshold is fixed. The examiner respectfully disagrees with the argument. As explained in the rejection of the claim, Ahmed clearly discloses, (see JP2003025953-Eng, pg.11, lines 13---32), if the occupant’ HLOC is larger than 110, the occupant’ AREA is larger than 1,000, and the occupant’ HEIGHT is larger than 100, then the occupant is an ADULT. On the other hand, if the occupant’ VLTR is among 35-115, the occupant’ AREA is larger than 1,000, and the occupant’ height is among 50-100, then the occupant is an ADULT. Therefore, Ahmed does not only teach using threshold values to identify adult but also teach these threshold values are vary based on other features.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit https://patentcenter.uspto.gov; https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center, and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
Prosecution Insights