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 .
DETAILED ACTION
The United States Patent & Trademark Office appreciates the application that is submitted by the inventor/assignee. The United States Patent & Trademark Office reviewed the following application and has made the following comments below.
Priority
This application claims benefit of foreign priority under 35 U.S.C. 119(a)-(d) of 10 2023 204 206.1, filed in Germany on 5/8/2023.
Amendment
Applicant submitted amendments on 6/11/2026. The Examiner acknowledges the amendment and has reviewed the claims accordingly.
Applicant Arguments:
In regards to Argument 1, Applicant/s state/s that the current amendments to the following claims, over comes the rejection, therefore, the rejection of 35 U.S.C. 103 should be removed.
Examiner’s Responses:
In response to Argument 1, Applicant’s arguments, see Remarks, filed 6/11/2026, with respect to the rejection(s) of claim(s) 1-9, 11-14 under 35 U.S.C. 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Wu (U.S. Patent No. 11,951,833, hereafter referred to as Wu) in view of RoyChowdhury et al (U.S. Patent Pub. No. 2022/0044034, hereafter referred to as Roy.).
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Or
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 14 is rejected under 35 U.S.C. 102(a) (2) as being anticipated by Wu (U.S. Patent No. 11,951,833, hereafter referred to as Wu).
In regards to Claim 14, Wu teaches a method for training a first neural network to assign pixels of monitoring data to components of the vehicle by semantic segmentation for use in a method for identifying a seat occupancy in a vehicle, the method for training comprising the following steps: training the first neural network to assign pixels of monitoring data to the components of the vehicle by semantic segmentation for use in the method for identifying the seat occupancy in the vehicle (col. 24 line 5-23, Wu teaches training CNN to detect objects different features in the image), the training comprising: receiving monitoring data of a camera of a vehicle interior (col. 45 line 45-65, Wu teaches using a camera, Figure 8.);
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; and learning, for each pixel in a frame, to which component of the vehicle the pixel is assigned (col. 46 line 40-65, Figure 8, Wu teaches segmentation and assign pixels to a group of the or individual passenger, and using a CNN to determine the body parts);
wherein, pixels are assigned to a vehicle seat which is arranged behind a person, wherein, it is recognized that the vehicle seat is arranged behind the person and the person is arranged in the vehicle seat (col. 47 line 50 – col. 48 line 20, Wu teaches detecting body parts to which seats in the vehicle.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived 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(a) 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.
Claims 1-9, 11-14 are rejected under 35 U.S.C. 103(a) as being unpatentable over Wu (U.S. Patent No. 11,951,833, hereafter referred to as Wu) in view of RoyChowdhury et al (U.S. Patent Pub. No. 2022/0044034, hereafter referred to as Roy.).
Regarding Claim 1, Wu teaches a method for identifying a seat occupancy in a vehicle, comprising the following steps: receiving monitoring data of a camera of a vehicle interior (col. 45 line 45-65, Wu teaches using a camera, Figure 8.);
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assigning pixels of the monitoring data to components of the vehicle by semantic segmentation using a first neural network (col. 46 line 40-65, Figure 8, Wu teaches segmentation and assign pixels to a group of the or individual passenger, and using a CNN to determine the body parts);
recognizing one or more persons in the monitoring data using a second neural network for recognizing a pose of a person (col. 45, lines 45-col. 46, line 10, col. 46 line 60-col. 47 line 20, Wu teaches recognizing individuals in the vehicles, the examiner interprets that by analysing the movement of an individual Wu can also determine the pose of the person, meaning their intention or direction.); and
merging the assigned pixels to the components of the vehicle with the recognized one or more persons, for identifying a seat occupancy in the vehicle (col. 47 line 50 – col. 48 line 5, Wu teaches determining different region of the passenger and combing the pixel regions, specifically, Figure 8 item 454c determine the rear passenger and then also item 520 determines that is the knee of the rear passengers, that is using different pixel groups to recognize and determine seat occupancy in the vehicle);
wherein, in the step of assigning pixels, pixels are assigned to a vehicle seat which is arranged behind a person, wherein, in the step of merging, it is recognized that the vehicle seat is arranged behind the person and the person is arranged in the vehicle seat (col. 47 line 50 – col. 48 line 20, col. 48 line 55-67, Wu teaches detecting body parts to which seats in the vehicle.).
Wu does not explicitly disclose using a first neural network or a second neural network.
Roy is in the same field of art of neural network object detection. Further, Roy teaches recognizing one or more persons in the monitoring data using a second neural network for recognizing a pose of a person (paragraph 26, paragraph 28, Roy teaches using a first and second CNN and the Examiner finds that adding a second CNN for recognizing a person would be interpreted to POSITA.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wu by modify the single neural network in Wu to include another or different neural network that is taught by Roy, to make the invention that captures video inside a vehicle and uses the a neural network to determine regions of a passenger or driver (Wu) and incorporating a second neural network to detect objects or features of a passenger or driver (Roy); thus, one of ordinary skilled in the art would be motivated to combine the references since image based detection of road hazards in general is difficult, time consuming, and not always accurate (paragraph 2, Roy) .
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
In regards to Claim 2, Roy in view of Wu discloses wherein, in the step of recognizing the one or more persons in the monitoring data, a two-dimensional pose of a person or a three-dimensional pose of the person is recognized (col. 23 lines 1 – 20, Wu teaches determine 3D direction.).
In regards to Claim 3, Roy in view of Wu discloses wherein, in the step of recognizing one or more persons in the monitoring data, a size of the one or more persons is recognized (col. 23 lines 1 – 20, Wu teaches determine 3D direction, also col. 47 line 50 – col. 48 line 20, Wu different size and regions of the passengers.).
In regards to Claim 4, Roy in view of Wu discloses wherein, in the step of recognizing one or more persons in the monitoring data, articulation points of the one or more persons are recognized (col. 23 line 1-35, col. 47 line 50 – col. 48 line 20).
In regards to Claim 5, Roy in view of Wu discloses wherein the first neural network for semantic segmentation is a trained neural network (col. 24, line 5-30, Wu teaches training the CNN for feature extractions.).
In regards to Claim 6, Roy in view of Wu discloses wherein the second neural network for recognizing a pose of a person is a trained neural network (paragraph 42 line 54-col. 43 line 10, Wu teaches determine the behavior of the passenger, which can be interpreted to the pose of the passenger.).
In regards to Claim 7, Roy in view of Wu discloses wherein, in the step of assigning pixels, a pixel- precise assignment of the monitoring data to components of the vehicle takes place per frame (col. 47 line 50 – col. 48 line 20, Wu teaches detecting body parts to which seats in the vehicle.).
In regards to Claim 8, Roy in view of Wu discloses wherein, in the step of assigning pixels, pixels are assigned to one or more components of the vehicle which are arranged in front of a person, wherein, in the step of merging, it is recognized that the person is arranged behind the one or more components of the vehicle (col. 47 line 50 – col. 48 line 20, Wu teaches detecting body parts to which seats in the vehicle, Figure 8.).
In regards to Claim 9, Roy in view of Wu discloses wherein, in the step of assigning pixels, pixels are assigned to a door of the vehicle which is arranged in front of a person, wherein, in the step of merging, it is recognized that the person is arranged behind the door outside the vehicle (col. 47 line 50 – col. 48 line 20, Wu teaches detecting body parts to which seats in the vehicle, Figure 8.).
In regards to Claim 11, Roy in view of Wu discloses wherein, in the step of assigning pixels, the pixels can be assigned to a vehicle seat pixel class, for recognizing which vehicle seat the pixels are assigned to (col. 47 line 50 – col. 48 line 20, Wu teaches detecting body parts to which seats in the vehicle, Figure 8.).
Regarding Claim 12, Wu teaches a system for identifying a seat occupancy in a vehicle, wherein the system is configured to:
receive monitoring data of a camera of a vehicle interior (col. 45 line 45-65, Wu teaches using a camera, Figure 8.);
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assign pixels of the monitoring data to components of the vehicle by semantic segmentation using a first neural network (col. 46 line 40-65, Figure 8, Wu teaches segmentation and assign pixels to a group of the or individual passenger, and using a CNN to determine the body parts);
recognize one or more persons in the monitoring data using a second neural network for recognizing a pose of a person (col. 45, lines 45-col. 46, line 10, col. 46 line 60-col. 47 line 20, Wu teaches recognizing individuals in the vehicles, the examiner interprets that by analysing the movement of an individual Wu can also determine the pose of the person, meaning their intention or direction.); and
merge the assigned pixels to the components of the vehicle with the recognized one or more persons, for identifying a seat occupancy in the vehicle (col. 47 line 50 – col. 48 line 5, Wu teaches determining different region of the passenger and combing the pixel regions, specifically, Figure 8 item 454c determine the rear passenger and then also item 520 determines that is the knee of the rear passengers, that is using different pixel groups to recognize and determine seat occupancy in the vehicle);
wherein, in assigning pixels, pixels are assigned to a vehicle seat which is arranged behind a person, wherein, in merging, it is recognized that the vehicle seat is arranged behind the person and the person is arranged in the vehicle seat (col. 47 line 50 – col. 48 line 20, Wu teaches detecting body parts to which seats in the vehicle.).
Wu does not explicitly disclose using a first neural network or a second neural network.
Roy is in the same field of art of neural network object detection. Further, Roy teaches recognizing one or more persons in the monitoring data using a second neural network for recognizing a pose of a person (paragraph 26, paragraph 28, Roy teaches using a first and second CNN and the Examiner finds that adding a second CNN for recognizing a person would be interpreted to POSITA.).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Wu by modify the single neural network in Wu to include another or different neural network that is taught by Roy, to make the invention that captures video inside a vehicle and uses the a neural network to determine regions of a passenger or driver (Wu) and incorporating a second neural network to detect objects or features of a passenger or driver (Roy); thus, one of ordinary skilled in the art would be motivated to combine the references since image based detection of road hazards in general is difficult, time consuming, and not always accurate (paragraph 2, Roy) .
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention
In regards to Claim 13, Roy in view of Wu discloses a camera configured to record the monitoring data of the vehicle interior (col. 45 line 45 – col. 46 line 5, Wu teaches using a camera).
Conclusion
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/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
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