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
This action is responsive to patent application as filed on 3/25/2024 which claims foreign priority to Japanese Pat. App. No: 2023-051692 filed 03/28/2023.
This action is made Non-Final.
Claims 1 – 20 are pending in the case. Claims 1, 12, and 20 are independent claims.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/25/2024 and 11/5/2024, is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Drawings
The drawings filed on 3/25/2024 have been accepted by the Examiner.
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 (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 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.
Claim(s) 1-5, 7 and 9-16, 19-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peng (USPUB 20220138459 A1) in view of Shimada et al (HULC: 3D Human Motion Capture with pose Manifold SampLing and Dense Contact Guidance, July 2022 from IDs filed 11/5/2024 hereinafter Shimada).
Claim 1:
Peng teaches An information processing apparatus comprising: at least one processor; and at least one memory storing executable instructions which, when executed by the at least one processor, cause the at least one processor to perform operations including: acquiring an image; detecting an entire human body from the acquired image; estimating a skeleton of the detected entire human body and generating skeleton information about the skeleton of the entire human body (Figs 1, 2 and 0018-19: For determining whether any abnormal situation occurs by recognizing the video, the user (or the administrator) has a frame of image in the video (or called a picture), and the image is used as the pending recognition image 100, such that the pending recognition image 100 can be determined whether any people in the image is in the abnormal state. In some embodiments, the pending recognition image 100 includes a human body image, for example, the human body image 110, 120, 130, and 140... The recognition system 200 of human body posture can automatically detect a human body posture of the image by recognizing a human body skeleton of the image); extracting a first feature quantity based on the generated skeleton information (0022: the storage device 230 stores a posture recognition model. After the posture recognition model inputs a skeleton image, a recognition result is outputted. For example, the posture recognition model stores a plurality of skeleton images and corresponding human body postures. When the pending recognition image is inputted into the posture recognition model and a determination is made that the pending recognition image includes the skeleton image, the skeleton image can be applied for recognizing the human body posture and the recognition result is outputted); extracting a second feature quantity (0023: the skeleton image that the processing device 220 needs for generating the posture recognition model from the pending recognition images includes one or more skeleton. The method for acquiring the skeleton image from the images is, for example, the human body keypoint detection algorithm. The human body keypoint detection algorithm is performed to detect the human body keypoint, such as the joints, to sketch the skeleton or each body part information of the human body); and estimating an orientation of the detected entire human body based on a third feature quantity in which the first and second feature quantities are connected (0024-25: One or more skeleton images can be captured from the pending recognition image, and each skeleton image of the pending recognition image will be inputted one-by-one into the posture recognition model for recognition... the skeleton image 310 of FIG. 3A and the skeleton image 320 of FIG. 3B correspond to the standing human body posture. The skeleton image 330 of FIG. 3C corresponds to the squatting human body posture. The skeleton image 340 of FIG. 3D corresponds to the falling-down human body posture. It should be noted that the skeleton images 310 to 340 of FIG. 3A to FIG. 3D are shown as embodiments. There are multiple skeleton images that one skeleton image corresponds to one human body posture in the posture recognition mode).
Peng, by itself, does not seem to completely teach extracting a second feature quantity based on a clipped image including the detected entire human body.
The Examiner maintains that these features were previously well-known as taught by Shimada.
Shimada teaches extracting a second feature quantity based on a clipped image including the detected entire human body (page 5: “our method takes an input a sequence...of T successive video frames from a static camera with known intrinsics...we detect a squared bounding box around the subject and resize the cropped image region...the background scene’s geometry that corresponds to the detected bounding box is represented by a single static point cloud; Shimada then proceeds to discuss the extraction of information based on the bounding box containing the human sample).
Peng and Shimada are analogous art because they are from the same problem-solving area, extracting posture information regarding people in an image.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Peng and Shimada before him or her, to combine the teachings of Peng and Shimada. The rationale for doing so would have been to specify a portion of an image from which to extract desired posture information.
Therefore, it would have been obvious to combine Peng and Shimada to obtain the invention as specified in the instant claim(s).
Claim 2:
Peng teaches estimating whether the orientation of the detected entire human body is an orientation in which at least three points of the human body are in contact with a ground (Fig 1 and 0033-39: Peng discusses determining if the posture of a human in the image is standing, squatting or laying down).
Claim 3:
Peng teaches estimating, based on the third feature quantity, relation information about a relation between the entire human body and a background in which the entire human body is excluded from the clipped image (0017 and Figs 3A-3D: the specific posture information from each of the 4 humans relates to how the posture is with respect to the background, despite the 4 humans being in clipped portions of an image not showing any of the scene of the image, only the 4 humans in their respective posture).
Claim 4:
Peng teaches the relation information includes information for dividing the clipped image into the entire human body and the background (0017 and Figs 3A-3D).
Claim 5:
Peng teaches the relation information includes information for dividing the clipped image into the entire human body and a floor surface (0017 and Figs 3A-3D).
Claim 7:
Peng teaches dividing the clipped image into an upper part of the human body, a lower part of the human body, a floor surface, and a wall surface (Fig 1, Figs 3A-3D, 0018: The method for acquiring the human body image will be described below. Many passengers in the MRT platform (shown as the human body image 110 and 120) are going into the carriage. Some passenger in the MRT platform (show as the human body image 130) is sitting on the floor. Some passenger in the MRT platform (shown as the human body image 140) is lying down on the floor...Figs 3A-3D present images of 4 distinct humans with upper and lower limbs on a background).
Claim 9:
Peng teaches the relation information includes depth information for the clipped image (0028).
Claim 10:
Peng teaches the extraction of the first feature quantity, the extraction of the second feature quantity, and the estimation of the human body are performed by using a trained neural network (0022).
Claim 11:
Peng teaches the estimation of the relation information is performed by using a trained neural network (0022).
Claim 12:
Claim 12 essentially recites a method for completing the steps of claim 1 and is therefore rejected over Peng and Shimada using the same rationale used in the rejection of claim 1.
Claims 13-16 and 19:
Claims 13-16 and 19 are essentially the same as claims 2-5 and 9, and are therefore rejected over Peng and Shimada using the same rationale used in the rejections of claims 2-5 and 9.
Claim 20:
Claim 20 essentially recites a non-transitory computer-readable storage medium storing a program for causing a computer to execute the steps of claim 1. As Peng teaches the claimed computer readable medium (0052), Claim 20 is rejected over Peng and Shimada using the same rationale used in the rejection of claim 1.
Claim(s) 8 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Peng in view of Shimada and further in view of Watanabe (USPUB 20190102613 A1).
Claims 8 and 18:
Peng in view of Shimada teaches every feature of claim 3.
Peng, by itself, does not seem to completely teach the relation information includes an imaging angle of the clipped image.
The Examiner maintains that these features were previously well-known as taught by Watanabe.
Watanabe teaches the relation information includes an imaging angle of the clipped image (0186 and 0255-258: when the range image camera 1 is at an angle apart from the head of the subject, it is necessary to estimate the joints of the whole body, so it is difficult for the limited resource in the range camera to perform the posture estimation processing because of a high load. However, it is possible to perform posture estimation with high accuracy by transmitting the range image data and estimating the posture in the posture estimation device. For example, when the range camera is installed at a high place and installed vertically downward, the whole body of the subject can be imaged by the range camera in the vicinity of the outer periphery of the FOV (Field Of View) of the range camera... even when the installation posture of the range image camera or the attitude of the target with respect to the range image camera changes, that is, when the imaging angle changes, if the same classifier is used, there is a possibility that the accuracy of the identification of the part or the joint is deteriorated... the posture estimator 23 may receive the imaging angle of the range image camera from the user via the communicator or the input device, or the imaging angle may be determined based on the shape of the marker or the like captured in the range image and the position of the foreground pixel).
Peng and Watanabe are analogous art because they are from the same problem-solving area, determine posture information regarding people in an image.
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Peng and Watanabe before him or her, to combine the teachings of Peng and Watanabe. The rationale for doing so would have been to better determine the posture of the people in the image.
Therefore, it would have been obvious to combine Peng and Watanabe to obtain the invention as specified in the instant claim(s).
Allowable Subject Matter
Claims 6 and 17 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Note
The Examiner cites particular columns, line numbers and/or paragraph numbers in the references as applied to the claims below for the convenience of the Applicant(s). Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the Applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the Examiner. See MPEP 2123.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure and is listed in the attached PTOL-892 form.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMED-IBRAHIM ZUBERI whose telephone number is (571)270-7761. The examiner can normally be reached on M-Th 8-6 Fri: 7-12/OFF.
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/MOHAMMED H ZUBERI/ Primary Examiner, Art Unit 2178