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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/16/2024 & 05/19/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. The title should be directed to context or inventive concept of the invention, such as estimating the 3D position of a person relative to a mobile robot. A new title is required that is clearly indicative of the invention to which the claims are directed.
The disclosure is objected to because of the following informalities:
[¶0003] of the specification state “…LiDAR is used to the detection of the position of a human…”. The grammar of the underlined portion is incorrect. The examiner believes this was intended to recite “…is used for the detection…”
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
Claim 1 recites the limitation, “a detection unit configured to detect…”
Claim 1 recites the limitation, “a determination unit configured to determine…”
Claim 1 recites the limitation, “an estimation unit configured to estimate…”
Claim 2 recites the limitation, “a transformer neural network configured to receive …”
Claim 4 recites the limitation, “a segmentation network configured to segment”
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The following limitations are computer-implemented means-plus-function limitations, wherein the corresponding structure and associated algorithm as disclosed in the specification for each limitation are being interpreted (see MPEP § 2181(B)):
“a detection unit” (element 31 of processing apparatus 30 in Fig. 2; ¶0022-25, 47)
The associated structure for the detection unit is a CPU that implements processing apparatus 30.
The associated function is as follows: The detection unit detects a bounding box B of a person P in an image I. The detection unit can perform object detection to detect a person, and then specifying a rectangular frame surrounding the person, which can be implemented with a machine learning model. This can also be down-sampled at a high speed by trimming the image, as the detection does not need to be performed at a high degree of precision.
“a determination unit” (element 32 of processing apparatus 30 in Fig. 2 which performs the determination process of Fig. 4; ¶0022, 26-29, 45, 47)
The associated structure for the determination unit is a CPU that implements processing apparatus 30.
The associated function is as follows: The determination unit determines whether or not a detected point in bounding box B corresponds to a person P. The determination unit uses a transformer (such as a transformer neural network generated by machine learning trained by self-supervised learning using knowledge distillation) which receives position data for each point including a detection direction and distance from the range sensor 13 and outputs binary data indicating if a point corresponds to a person, which can be represented with a 1 for when a person is detected and 0 for when a person is not detected. The detection unit performs the process of Fig. 4 by inputting position data of N detected points into transformer 321.
“an estimation unit” (element 33 of processing apparatus 30 in Fig. 2; ¶0022, 30-31, 47)
The associated structure for the estimation unit is a CPU that implements processing apparatus 30.
The associated function is as follows: The estimation unit estimates the 3D position of the person P based on distances of detected points DP1, such as the median distances of a plurality of detected points DP1 from range sensor 13 to person P. It calculates the 3D coordinates of the person P based on the distance, and specifies a direction to the person based on the image or position data.
“a transformer neural network” (element 321 of Fig. 4; ¶0026-29, 31-34, 43-44, 47)
The associated structure for the transformer neural network is a CPU that implements processing apparatus 30.
The associated function is as follows: Used by the determination unit, the transformer neural network, or transformer 321 which receives position data including a direction and distance from range sensor 13 to output binary data (binomial classification) relating to whether N number of detected points belongs to a person. The transformer may be generated by self-supervised learning using knowledge distillation. This transformer can be implemented by the robot itself or implemented online.
“a segmentation network” (element 41 of Fig. 5; ¶0034-37, 47)
The associated structure for the segmentation is a CPU that implements processing apparatus 40.
The associated function is as follows: Implemented by the processing apparatus 40 for generating training data, the segmentation network 41 segments a person in an image taken by a camera by classifying each pixel in an Image I into a respective class, and predicts a class label for all objects included in the image by instance segmentation. The segmentation network specifies pixels corresponding to a person P.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112(b)
Claim 6 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation “the transformer neural network”. There is insufficient antecedent basis for this limitation in the claim. The transformer neural network has not been properly introduced in chain of dependency, and should instead be first introduced as “a transformer neural network…”.
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.
Claim(s) 5 & 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wakayama et al (US 2020/0311400 A1), hereinafter referred to as "Wakayama".
Regarding claim 5, Wakayama teach A method for estimating a 3D position of a person using a computer (Fig. 5 outlines a series of operations for driving assistance which involve identifying the 3D position of a human body implemented by the driving assistance device 10 [0052-74; Fig. 5]), comprising:
detecting a distance to a nearby point by using a 2D range sensor installed in a mobile robot (the driving device 10 utilizes an environment sensor 20 (formed by a camera 44, a radar 46, and a LiDAR 48) on a host vehicle [0021-24], the radar of 46 can implement the distance measurement steps of steps S4-S5 [0062-63; Fig. 5]);
taking an image of an area around the mobile robot by a camera (in step S1, an image around the host vehicle is taken by camera 44 [0053; Fig. 5]);
detecting a bounding box surrounding a person included in the image (steps S2-S3 detect a person in the image [0054-55; Fig. 5], during the recognition process a rectangular frame 80 "BBOX" surrounds a human body 70 [0048; Fig. 3]);
determining, for each point included in the bounding box detected by the 2D range sensor, whether or not the detected point corresponds to the person (in step S3, a quasi-skeleton estimation unit 66c determines if a human body characteristic parts in the frame 80 can be detected, and if they can, steps S32-S34, estimates points that correspond to the body (quasi-joint parts 82b, human body characteristic parts 82a, fleshed parts 84, and axis parts 82c) [0055-061; Fig. 4 & 6]); and
estimating a 3D position of the person based on a distance to a detected point determined to correspond to the person (the LiDAR 48, which captures 3D positional data of the human body 70 can be used to perform the distance measurement processes outlined in steps S4-S5 [0062-064; Fig. 5], estimating the three-dimensional position of the human body [0078]).
Regarding claim 9, Wakayama teach A non-transitory computer readable medium storing a program for causing a computer to perform a method (Wakayama: the control system 16 contains one or more CPUs configured to execute programs stored on the non-transitory storage device 52 [¶0035; Fig. 1]) according to Claim 5 (as described previously).
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) 1-3 & 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wakayama et al (US 2020/0311400 A1), hereinafter referred to as “Wakayama” in view of Park et al (“An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network”, Sensors, 2021), hereinafter referred to as “Park”.
With respect to claim 1, Wakayama teach A system (driving assistance device 10 [0020; Fig. 1]) comprising:
a mobile robot including a 2D (two-dimensional) range sensor configured to detect a distance to a nearby point (the driving device 10 utilizes an environment sensor 20 (formed by a camera 44, a radar 46, and a LiDAR 48) on a host vehicle [¶0021-24], the radar of 46 can implement the distance measurement steps of steps S4-S5 [¶0062-63; Fig. 5]);
a camera configured to take an image of an area around the mobile robot (in step S1, an image around the host vehicle is taken by camera 44 [¶0053; Fig. 5]);
a detection unit† (†limitations interpreted under 35 U.S.C. § 112(f) are being interpreted in accordance with their associated structure and function in accordance with the disclosure of the present application) configured to detect a bounding box surrounding a person included in the image (implemented by a plurality of CPUs for control system 16 [¶0035; Fig. 1], the human body specifying unit 66b and quasi-skeleton estimation unit 66c extracts information about the human body per performing steps S2-S3to detect a person in the image [¶0054-55; Fig. 5], during the recognition process a rectangular frame 80 "BBOX" surrounds a human body 70 [¶0048; Fig. 3], the quasi-skeleton estimation unit 66 can be implemented using a deep learning (machine learning source) such as "OpenPose" [¶0058]);
and
an estimation unit† configured to estimate a 3D (three-dimensional) position of the person based on a distance to a detected point determined to correspond to the person (implemented by a plurality of CPUs for control system 16 [¶0035; Fig. 1], the external environment information acquisition unit 66a obtains positional data of a person by LiDAR 48, which captures 3D positional data of the human body 70 can be used to perform the distance measurement processes outlined in steps S4-S5 [¶0062-064; Fig. 5], estimating the three-dimensional position of the human body [¶0078]).
While Wakayama teach detecting a person inside a bounding box for a distance from the sensor, their quasi-skeleton estimation unit 66c [¶0083] fails to disclose utilizing a transformer neural network trained via knowledge distillation. Park, on the other hand, is analogous art pertinent to the field of endeavor of the present application and describe knowledge distilled lightweight top-down pose network. More particularly park teach a determination unit† configured to determine, for each point included in the bounding box detected by the 2D range sensor, whether or not the detected point corresponds to the person; (Park: object detection is conducted using YOLOv3 wherein detected images are passed through a spatial transformer network to select areas of interest and detection information of a human is extracted in the form of a heatmap representing location information of the human body joints [Sec 3.2: Preliminary Processing - ¶02; Fig. 2], the spatial transformer network is trained using a teacher/student knowledge distillation method [Sec 3.1: Overview - ¶01-02; Sec 3.4: Knowledge Distillation Method - ¶01-07; Fig. 2]).
Park remark that their method utilizes knowledge distillation to reduce computational complexity while minimizing any degradation in performance [Sec: Abstract]. It would have been obvious to one of ordinary skill to utilize the positional data obtained by the LiDAR of Wakayama to inform the spatial transformer network to inform the YOLOV3 object detection of Park to efficiently and accurately classify 3D points in a human region as belonging to a human.
As for claim 2, Wakayama in view of Park teach The system according to claim 1 (as described above), and while Wakayama generally teaches utilizing an angle and distance from a radar sensor (the angle derived from the 3D dimensional LiDAR sensor data) for a pose estimation method based on deep learning like "OpenPose" [¶0058, 62-65], they fail to disclose an explicit transformer neural network for determining whether a point corresponds to a person.
Park, however, teach wherein the determination unit is a transformer neural network† configured to receive position data including a detecting direction and a distance from the 2D range sensor and output binary data indicating whether or not each of the detected points corresponds to the person (Park: object detection is conducted using YOLOv3 wherein detected images are passed through a spatial transformer network to select areas of interest and detection information of a human is extracted in the form of a heatmap representing location information of the human body joints - the examiner notes that a heatmap, under broadest reasonable interpretation can in its simplest form can be presented with binary binning for whether a point corresponds to a human or not [Sec 3.2: Preliminary Processing - ¶02; Fig. 2]).
Park remark that their method utilizes knowledge distillation to reduce computational complexity while minimizing any degradation in performance [Sec: Abstract]. It would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the positional data obtained by the LiDAR of Wakayama to inform Park's spatial transformer network utilizing YOLOV3 object detection to efficiently and accurately classify 3D points in a human region as belonging to a human.
Turning to claim 3, Wakayama in view of Park teach The system according to claim 2 (as described above), wherein the transformer neural network is a machine learning model trained through self-supervised learning using knowledge distillation (Park: the spatial transformer network is trained using a teacher/student knowledge distillation method [Sec 3.1: Overview - ¶01-02; Sec 3.4: Knowledge Distillation Method - ¶01-07; Fig. 2]).
Park remark that their method utilizes knowledge distillation to reduce computational complexity while minimizing any degradation in performance [Sec: Abstract]. It would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the positional data obtained by the LiDAR of Wakayama to inform Park's spatial transformer network utilizing YOLOV3 object detection to efficiently and accurately classify points in a human region as belonging to a human.
With respect to claim 6, Wakayama teach The method according to claim 5 (as described previously), but fails to disclose a transformer neural network for discerning whether a point corresponds to a human. Park, on the other hand, teach wherein the transformer neural network determines whether or not the detected point corresponds to the person, and the transformer neural network is a transformer neural network configured to receive position data including an angle and a distance from the 2D range sensor and output binary data indicating whether or not each of the detected points corresponds to the person (Park: object detection is conducted using YOLOv3 wherein detected images are passed through a spatial transformer network to select areas of interest and detection information of a human is extracted in the form of a heatmap representing location information of the human body joints - the examiner notes that a heatmap, under broadest reasonable interpretation can in its simplest form can be presented with binary binning for whether a point corresponds to a human or not [Sec 3.2: Preliminary Processing - ¶02; Fig. 2]).
Park remark that their method utilizes knowledge distillation to reduce computational complexity while minimizing any degradation in performance [Sec: Abstract]. It would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the positional data obtained by the LiDAR of Wakayama to inform Park's spatial transformer network utilizing YOLOV3 object detection to efficiently and accurately classify 3D points in a human region as belonging to a human.
Concerning claim 7, Wakayama in view of Park teach The method according to claim 6 (as described above), wherein the transformer neural network is a machine learning model trained through self-supervised learning using knowledge distillation (Park: the spatial transformer network is trained using a teacher/student knowledge distillation method [Sec 3.1: Overview - ¶01-02; Sec 3.4: Knowledge Distillation Method - ¶01-07; Fig. 2]).
Park remark that their method utilizes knowledge distillation to reduce computational complexity while minimizing any degradation in performance [Sec: Abstract]. It would have been obvious to one of ordinary skill before the effective filing date of the present application to utilize the positional data obtained by the LiDAR of Wakayama to inform Park's spatial transformer network utilizing YOLOV3 object detection to efficiently and accurately classify points in a human region as belonging to a human.
Claim(s) 4 & 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wakayama et al (US 2020/0311400 A1), hereinafter referred to as “Wakayama” in view of Park et al (“An Efficient Approach Using Knowledge Distillation Methods to Stabilize Performance in a Lightweight Top-Down Posture Estimation Network”, Sensors, 2021), hereinafter referred to as “Park”, further in view of Uday et al (“Estimating 3D Human Pose using Point Based Pose Estimation and Single Stage Method”, ICAN, 2022), hereinafter referred to as “Uday”.
Concerning claim 4, Wakayama in view of Park teach The system according to claim 3 (as described previously), and while Park teach obtaining learning data for their transformer neural network, they fail to teach it is obtained via an explicit segmentation network leveraging a feature estimator and clustering algorithm for extracting a detected point located at an ankle of a person.
Uday, per contra, describe a single-stage 3D human pose estimation method for semantic segmentation and clustering of human joints. More specifically, Uday teach wherein learning data of the transformer neural network is data obtained by a segmentation network† configured to segment the person shown in the image taken by the camera (a point-based pose estimator is leveraged with the spatial transformer network of the PoseNet Architecture for semantic segmentation of human poses [Sec III. Proposed Method - Subsec A-B.; Fig. 1]) and a keypoint estimator combined with a clustering algorithm for extracting a detected point located at an ankle of the person (a regression model is used to estimate 3D joint locations (such as an ankle, wherein features are extracted using a multi-layer perceptron (MLP) [Sec III. Proposed Method - Subsec C.; Fig. 2], and K-means clustering was performed on poses in the training sets to obtain 100 poses serving as cluster centroids [Sec IV. Experiments and Results - Subsec B.]).
A key advantage highlighted by Uday is that their deep neural regression network can predict positions of a person's body without joints being visible in the image, with an explicit joint-detection system to determine which critical body joints are present in an image [Sec I. Introduction - 07]. One of ordinary skill in the art before the effective filing date of the present application would recognize the advantage of implementing the single-stage pose estimation taught by Uday to provide the training data to the spatial transformer network of Wakayama in view of Park to better inform key joint positioning during knowledge distillation.
As for claim 8, Wakayama in view of park teach The method according to claim 6 (as described previously), and while Park teach obtaining learning data for their transformer neural network, they fail to teach it is obtained via an explicit segmentation network leveraging a feature estimator and clustering algorithm for extracting a detected point located at an ankle of a person.
Uday, however, teach wherein learning data of the transformer neural network is data obtained by a segmentation network† configured to segment the person shown in the image taken by the camera (a point-based pose estimator is leveraged with the spatial transformer network of the PoseNet Architecture for semantic segmentation of human poses [Sec III. Proposed Method - Subsec A-B.; Fig. 1]) and a keypoint estimator combined with a clustering algorithm for extracting a detected point located at an ankle of the person (a regression model is used to estimate 3D joint locations (such as an ankle, wherein features are extracted using a multi-layer perceptron (MLP) [Sec III. Proposed Method - Subsec C.; Fig. 2], and K-means clustering was performed on poses in the training sets to obtain 100 poses serving as cluster centroids [Sec IV. Experiments and Results - Subsec B.]).
A key advantage highlighted by Uday is that their deep neural regression network can predict positions of a person's body without joints being visible in the image, with an explicit joint-detection system to determine which critical body joints are present in an image [Sec I. Introduction - 07]. One of ordinary skill in the art before the effective filing date of the present application would recognize the advantage of implementing the single-stage pose estimation taught by Uday to provide the training data to the spatial transformer network of Wakayama in view of Park to better inform key joint positioning during knowledge distillation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Maji et al (US 2023/0137337 A1) disclose a key-point detection system leveraging machine learning for joint detection and pose estimation.
Sandahl et al (US 2013/0329960 A1) describe a method for determining an angle and distance a person is away from a vehicle for estimating joint positioning.
Choi et al (US 2025/0148801 A1) teach an apparatus for controlling a vehicle using LiDAR to detect and track 3D objects and associated keypoints.
Sun et al (“A dynamic keypoint selection network for 6DOF pose estimation”, Image and Vision Computing, 2022) outline a method for semantic segmentation during pose estimation via selection of keypoints from a foreground feature map.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael M. Sofroniou whose telephone number is (571)272-0287. The examiner can normally be reached M-F: 8:30 AM - 5:00 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John M. Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL M SOFRONIOU/Examiner, Art Unit 2661
/AARON W CARTER/Primary Examiner, Art Unit 2661
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