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 .
Response to Arguments
Applicant’s arguments, see Remarks page 8, filed 07/07/2025, with respect to the rejections of claims 1-7 and 10-16 under 35 U.S.C. 101 have been fully considered and are persuasive. The rejections of claims 1-7 and 10-16 have been withdrawn.
Applicant's arguments, see Remarks pages 8-9, filed 07/07/2025, with respect to the claim limitations interpreted under 35 U.S.C. 112(f) have been fully considered but they are not persuasive.
On pages 8-9, Applicant argues:
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Examiner respectfully disagrees.
MPEP 2181 subsection I discloses: “The USPTO must apply 35 U.S.C. 112(f) in appropriate cases, and give claims their broadest reasonable interpretation (BRI), in light of and consistent with the written description of the invention in the application. In determining the BRI, examiners should establish the meaning of each claim term consistent with the specification as it would be interpreted by one of ordinary skill in the art, including identifying and construing functional claim limitations. If a claim limitation recites a term and associated functional language, the examiner should determine whether the claim limitation invokes 35 U.S.C. 112(f). Application of 35 U.S.C. 112(f) is driven by the claim language, not by applicant’s intent or mere statements to the contrary included in the specification or made during prosecution. See In re Donaldson Co., 16 F.3d at 1194, 29 USPQ2d at 1850 (stating that 35 U.S.C. 112, sixth paragraph "merely sets a limit on how broadly the PTO may construe means-plus-function language under the rubric of reasonable interpretation’").”
The interpretation of a claim limitation under 35 U.S.C. 112(f) is performed if the claim limitations meets the following requirements set forth by 3-prong analysis:
(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.
The limitations pertaining to “an image acquisition unit,” in claim 1, and “a controller,” in claims 1, 4-8, 10, and 13-17, meet the requirements of the 3-prong analysis.
Regarding the limitation, “an image acquisition unit configured to acquire an image of an area in front of the host vehicle:”
(A): The “image acquisition unit” is a generic placeholder term that has no specific structural meaning, it is simply a substitute for the term “means.”
(B): The “image acquisition unit” is modified by functional language, linked by the transition phrase “configured to.”
(C): The “image acquisition unit” is not modified by any sufficient structure.
Regarding the limitation, “a controller…” present in claim 1:
(A): The “controller” is a generic placeholder term that has no specific structural meaning, it is simply a substitute for the term “means.”
(B): The “controller” is modified by functional language, linked by the transition phrase “configured to.”
(C): The “controller” is not modified by any sufficient structure.
In addition, the arguments for interpreted the “controller” limitations under 112(f) in claim 10 are analogous, wherein depending claims 4-8 and 13-17 fail to cure the deficiencies of parent claims 1 and 10, respectively.
Therefore, “…an image acquisition unit” in claim 1, and “…a controller,” in claims 1, 4-8, 10, and 13-17, are interpreted under 35 U.S.C. 112(f).
Applicant’s arguments, see Remarks pages 9-10, filed 07/07/2025, with respect to the rejection of amended claim(s) 1 and 10 under 35 U.S.C. 102(1)(a) have been fully considered and are moot in view of the new grounds of rejection (detailed in the rejections below) necessitated by Applicant’s amendment to the claim(s).
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:
“…an image acquisition unit” in claim 1.
“…a controller” in claims 1, 4-8, 10, and 13-17.
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.
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 § 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.
Claim(s) 1, 4-5, 10, and 13-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (Front and rear vehicle detection and tracking in the day and night times using vision and sonar sensor fusion) hereinafter referenced as Kim, in view of Ng et al. (BEV-Seg: Bird’s Eye View Semantic Segmentation Using Geometry and Semantic Point Cloud) hereinafter referenced as Ng, Nix et al. (US20090254260A1) hereinafter referenced as Nix, and Takahata et al. (US 20240427016 A1) hereinafter referenced as Takahata.
Regarding claim 1, Kim discloses: An apparatus for controlling activation of an object detection sensor for detecting an object in front of a host vehicle (Kim: Abstract: “This paper proposes new robust vehicle detection and tracking method regardless of the light and road conditions at any distance using vision and sonar sensors. We use the sonar sensor for detection and distance estimation within 10m and use image sensor over 10m.”), the apparatus comprising:
an image acquisition unit configured to acquire an image of an area in front of the host vehicle (Kim: Figure 1; Page 2: Col 1: “The two cameras that can detect vehicles in the medium and far range are installed by the side of a rear-view mirror and at the ceiling above the back seat…we acquire 2 images of 1 field with 640×240 for avoiding the motion flow and use 320×240 image by sub-sampling…”; Wherein the camera by the rear-view mirror captures images in front of the vehicle.); and
a controller (Kim: Figure 1: Computer) configured to control the activation of the object detection sensor based on a presence or absence of a free space in front of the host vehicle that is determined from the image of the area in front of the host vehicle (Kim: Figure 7; Page 3: Col 2: “Above 10m of distance, this system can extract vehicle candidates by just image, but below 10m of distance, the vehicle detection is supported by a sonar sensor.”; Wherein the sonar is activated based on whether there is 10 meters of ‘free space’ in front of the vehicle) and based on a relative kinematic relationship between a preceding vehicle extracted from the image of the area in front of the host vehicle and the host vehicle (Kim: Figure 5; Page 3: Col 2: “And because DAS is normally interested in the nearby vehicles that drive in the current driving, left and right lanes, and this system remove other vehicles that are not of interest.”; Wherein the system is interested in vehicles driving in the same lane, and thus the same direction of travel.), wherein:
when determining the presence or absence of the free space in front of the host vehicle, by analyzing a number of pixels of the image of the area in front of the host vehicle, the controller is configured to determine whether a central area in front of the host vehicle, a left-side area to the left of the central area, and a right- side area to the right of the central area correspond to the free space (Kim: Figure 5; Page 3: Col 1: “As the shadow region between the vehicle and the road appears in the day time regardless of weather conditions or low light conditions, and is also a valid region for distance estimation because of plan world assumption, we use the shadow region as the first feature of a vehicle …If the lane information does not exist like the failure of the lane detection or due to an intersection, etc., three virtual lanes are generated instead with the proper lane width estimated from the ego-vehicle position used by the splitting and filtering of shadow regions.”; Page 3: Col 2: “And because DAS is normally interested in the nearby vehicles that drive in the current driving, left and right lanes, and this system remove other vehicles that are not of interest.”; Wherein the system determines if the left, right, and current lanes have vehicles in them, and determines their distances based on their shadow’s image coordinates.).
Kim does not disclose expressly: in a case where a ratio of the number of pixels of an image of each of the central area, the left-side area, and the right-side area in which of each the object is detected to the number of the pixels of the image of the area in front of the host vehicle is lower than a preset reference value, the controller is configured to determine that the corresponding area in front of the host vehicle corresponds to the free space.
Ng discloses a method for processing RGB images taken by a vehicle at various angles and predicts semantically segmented images and depth maps from each image. The images are then processed into a semantic point cloud, which is then converted into a semantically segmented bird’s-eye view image (Ng: 3.1. A Two-Stage Perception Pipeline). Wherein the birds-eye semantic segmentation image captures an area of 15x15 meters around the vehicle (Ng: 4. Experiment Results: “To capture bird’s-eye semantic segmentation, we place an imaginary camera 200m above the ground facing down. The segmentation is captured with the field of view of 8.58 degree and at the resolution of 256×256, covering an area of 15m × 15m”), and the depths of the objects estimated by the depth estimation network are retained within the pixels of the BEV image (Ng: Abstract: “Our novel 2-staged perception pipeline explicitly predicts pixel depths and combines them with pixel semantics in an efficient manner, allowing the model to leverage depth information to infer objects’ spatial locations in the BEV.” ).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute the algorithm for detecting the distance of vehicles in the left, center, and right lanes using images disclosed in Kim by detecting whether objects are present within the 15m x 15m bird’s eye view semantic segmentation image produced by the Bird’s Eye view semantic segmentation method taught by Ng. The suggestion/motivation for doing so would have been “We note that the baseline is unable to predict many of the important classes, such as pedestrians and cyclists. On the other hand, our approach not only recognizes these important objects, but also recognizes road lanes and stop signs...In general, for smaller and subtler objects, our approach outperforms the baseline by a large margin.” (Ng: 4. Experiment Results; Wherein the semantic segmentation method is able to accurately detect large and small objects and their distances alike). Further, one skilled in the art could have substituted the elements as described above by known methods with no change in their respective functions, and the substitution would have yielded nothing more than predictable results.
Kim in view of Ng does not disclose expressly: wherein the object detection sensor is a front-side ultrasonic sensor; after the host vehicle moves a preset distance by traveling at a low vehicle speed within a preset speed range, the controller is configured to start to perform an operation of controlling activation of the front-side ultrasonic sensor.
Nix discloses a front-side ultrasonic sensor (Nix: Figure 1; 0031: “In the exemplary embodiment there are four short-range sensors, shown individually at 24 a, 24 b, 24 c and 24 d…The short-range sensors 24 may be any suitable type of sensors, such as piezo-electric ultrasonic sensors. Thus, the short-range sensors 24 may be referred to as ultrasonic sensors and the short-range sensing system 25 may be referred to as the ultrasonic sensing system 25.”), after the host vehicle moves a preset distance by traveling at a low vehicle speed within a preset speed range, the controller is configured to start to perform an operation of controlling activation of the front-side ultrasonic sensor (Nix: 0034: “the ultrasonic short range distance sensing system 25 may, for example, only be used when the speed of the host vehicle 10 is below a threshold. The ultrasonic sensing system 25 may be turned off if the host vehicle 10 exceeds a first speed threshold speed Voff and turned on if the speed of the host vehicle 10 falls below a second speed threshold Von.”; Wherein the preset distance is determined by detection of the host vehicle’s speed).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute the sonar sensors disclosed by Kim in view of Ng with the ultrasonic sensors with pulse cycles determined by vehicle speed taught by Nix. The suggestion/motivation for doing so would have been “To detect objects that are further away from the host vehicle a long ultrasonic pulse of about 20 cycles may be used. To detect object very close to the host vehicle a short ultrasonic pulse of about 8 cycles may be used…if the speed of the host vehicle 10 falls below a second, lower, speed threshold, short pulses (eg. of 8 cycles) may be used.” (Nix: 0039-0041; Wherein the sensor may be adjusted based on distance detection needs). Further, one skilled in the art could have substituted the elements as described above by known methods with no change in their respective functions, and the substitution would have yielded nothing more than predictable results.
Kim in view of Ng and Nix does not disclose expressly: and a rear-side ultrasonic sensor for detecting the object behind the preceding vehicle is installed in the preceding vehicle, and in a case where an output signal value of the front-side ultrasonic sensor that is initially set to be in a reception mode is at or above a preset reference value…the controller is configured to start to perform an operation of controlling activation of the front-side ultrasonic sensor.
Takahata discloses a front-side ultrasonic sensor (Takahata: 0050: “The vehicle 1 also includes a first front center sonar 21a, a second front center sonar 21b, a first front corner sonar 21c, a second front corner sonar 21d, a first rear center sonar 22a, a second rear center sonar 22b, a first rear corner sonar 22c, and a second rear corner sonar 22d.”; 0055: “The sonars 21 and the sonars 22 transmit ultrasonic waves subjected to frequency modulation.”), and a rear-side ultrasonic sensor for detecting the object behind the preceding vehicle is installed in the preceding vehicle (Takahata: Figure 9; 0120: “the sonar 22 receives an interference wave such as an ultrasonic wave generated by the sonar 22 of the vehicle 1B or a reflected wave of the ultrasonic wave before receiving the reflected wave reflected by the vehicle 1B.”; Wherein the sonar of vehicle 1B is a rear-side ultrasonic sensor), and in a case where an output signal value of an ultrasonic sensor that is initially set to be in a reception mode is at or above a preset reference value, a controller is configured to start to perform an operation of controlling activation of the ultrasonic sensor (Takahata: 0006: “The interference detection circuit, in operation, detects, as interference, another transmitted wave signal transmitted from another transmission circuit different from the transmission circuit, based on the signal received by the reception circuit. The determination circuit, in operation, determines a frequency modulation pattern to be applied to the transmitted wave signal and a transmission timing of the transmitted wave signal, based on detection results of the object detection circuit and the interference detection circuit.”; Wherein the transmitted wave signal of the ultrasonic sensor is modified).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the interference detection circuit taught by Takahata to detect an interference from a preceding vehicle and control the activation of the frontside ultrasonic sensors disclosed by Kim in view of Ng and Nix. The suggestion/motivation for doing so would have been “The present disclosure provides an obstacle detection device capable of reducing the possibility of erroneous detection of an obstacle due to an interference wave.” (Takahata: 0005). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kim in view of Ng and Nix with Takahata to obtain the invention as specified in claim 1.
Regarding claim 4, Kim in view of Ng, Nix, and Takahata discloses: The apparatus of claim 1, wherein, when determining the presence or absence of the free space in front of the host vehicle, the controller is configured to convert the image of the area in front of the host vehicle to a bird-eye view (Ng: Figure 2; 3.1. A Two-Stage Perception Pipeline: “Given N RGB images captured at different angles, we pass each into a semantic segmentation network and a monocular depth estimation network…Finally, we remove the height dimension y by orthographically projecting the points downward onto an incomplete bird’s-eye segmentation T of size HBEV × WBEV .”) and then determine a presence or absence of the free space in front of the host vehicle, using a technique of applying a predefined image processing algorithm to the bird-eye view resulting from the converted image and performing analysis of the number of pixels of the image of the area in front of the host vehicle (Ng: Figure 2; 3.1. A Two-Stage Perception Pipeline: “We expand the incomplete bird’s eye segmentation T into one-hot encoding along the class c dimension…This is passed into a parser network to predict the full bird’s-eye semantic segmentation. Our parser network is another semantic segmentation network P.”; 4. Experiment Results: “The segmentation is captured with the field of view of 8.58 degree and at the resolution of 256×256, covering an area of 15m × 15m…We note that the baseline is unable to predict many of the important classes, such as pedestrians and cyclists. On the other hand, our approach not only recognizes these important objects, but also recognizes road lanes and stop signs”; Wherein the semantically segmented bird’s eye image is able to detect road lanes or obstacles, such as pedestrians or vehicles, within a 15m x 15m area around the vehicle).
Ng discloses: converting the images of the area around the host vehicle to a bird-eye view and then determine a presence or absence of the free space in around the host vehicle, using a technique of applying a semantic segmentation image processing algorithm to the bird-eye view resulting from the converted image and performing analysis of the number of pixels of the image of the area around the host vehicle (Ng: Figure 2; Page 5: Col 1: “We expand the incomplete bird’s eye segmentation T into one-hot encoding along the class c dimension…This is passed into a parser network to predict the full bird’s-eye semantic segmentation. Our parser network is another semantic segmentation network P.”; Page 6: Col 2: “We note that the baseline is unable to predict many of the important classes, such as pedestrians and cyclists. On the other hand, our approach not only recognizes these important objects, but also recognizes road lanes and stop signs”; Wherein the incomplete bird’s eye image is converted to a semantically segmented image which may be analyzed for road lanes or obstacles, such as pedestrians or vehicles).
Regarding claim 5, Kim in view of Ng, Nix, and Takahata discloses: The apparatus of claim 1, wherein, in a case where the area in front of the host vehicle does not correspond to the free space, the controller is configured to primarily determine that the object detection sensor needs to be activated (Kim: Page 3: Col 2: “Above 10m of distance, this system can extract vehicle candidates by just image, but below 10m of distance, the vehicle detection is supported by a sonar sensor…when the distance is above 3m, the sonar distance is fused with the distance calculated from the image coordinates.”).
As per claim(s) 10, arguments made in rejecting claim(s) 1 are analogous.
As per claim(s) 13, arguments made in rejecting claim(s) 4 are analogous.
As per claim(s) 14, arguments made in rejecting claim(s) 5 are analogous.
Claim(s) 6-7 and 15-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ng, Nix and, Takahata, and further in view of Kim (KR 20150084730 A) hereinafter referenced as Kim, Myong.
Regarding claim 6, Kim in view of Ng, Nix and Takahata discloses: The apparatus of claim 5.
Kim in view of Ng, Nix and Takahata does not disclose expressly: wherein the controller is configured to extract the preceding vehicle from the image of the area in front of the host vehicle by applying a predefined object detection algorithm to the image of the area in front of the host vehicle and analyze a relative kinematic relationship between the extracted preceding vehicle and the host vehicle based on an angle between traveling directions of the preceding vehicle and the host vehicle and based on whether the traveling directions of the preceding vehicle and the host vehicle are opposite to each other.
Kim, Myong discloses: wherein the controller is configured to extract the preceding vehicle from the image of the area in front of the host vehicle by applying a predefined object detection algorithm to the image of the area in front of the host vehicle (Kim, Myong: Abstract: “…the vehicle detection device detects a light source of a vehicle on a front side thereof by analyzing an image obtained by photographing the front side of the vehicle.”; Page 3: Paragraph 4: “The control unit 40 analyzes the image collected through the image collecting unit 30 to determine whether a vehicle exists around the light source.”) and analyze a relative kinematic relationship between the extracted preceding vehicle and the host vehicle based on an angle between traveling directions of the preceding vehicle and the host vehicle and based on whether the traveling directions of the preceding vehicle and the host vehicle are opposite to each other (Kim, Myong: Page 3: Paragraph 5: “The controller 40 converts the headlight 60 into a high beam if it is determined that the vehicle does not exist as a result of the image analysis, and converts the headlight 60 into a low beam when it is determined that the vehicle exists as a result of the image analysis. At this time, the control unit 40 analyzes the image collected through the image collecting unit 30, determines the presence of the vehicle headlight corresponding to the image feature formed by the light filtered by the light scattering”; Wherein the determination of whether a vehicle is present and the presence of its headlights constitutes determining the angle of traveling direction of both the preceding and host vehicles and determining whether the traveling directions are opposite to one another).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to implement the object detection algorithm used for controlling the headlight high beams taught by Kim, Myong to the captured frontal images control the sonar sensors disclosed in Kim in view of Ng, Nix and Takahata. The suggestion/motivation for doing so would have been “…if there is no vehicle ahead, the headlight beam is converted into a high beam, and if it is detected that there is a vehicle ahead, the headlight beam is converted into a low beam.” (Kim, Myong: Page 2: Paragraph 3; Wherein the sensor is deactivated in order to not interfere with the driver of the vehicle driving in the opposite direction). Further, one skilled in the art could have combined the elements as described above by known methods with no change in their respective functions, and the combination would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kim in view of Ng, Nix and Takahata with Kim, Myong to obtain the invention as specified in claim 6.
Regarding claim 7, Kim in view of Ng, Nix, Takahata, and Kim, Myong discloses: The apparatus of claim 6, wherein, in a case where the angle between the traveling directions of the preceding vehicle and the host vehicle is at or above a preset reference value or in a case where the host vehicle travels forward and where the preceding vehicle travels backward, the controller is configured to secondarily determine that the object detection sensor needs to be deactivated (Kim, Myong: Page 3: Paragraph 5: “The controller 40 converts the headlight 60 into a high beam if it is determined that the vehicle does not exist as a result of the image analysis, and converts the headlight 60 into a low beam when it is determined that the vehicle exists as a result of the image analysis. At this time, the control unit 40 analyzes the image collected through the image collecting unit 30, determines the presence of the vehicle headlight corresponding to the image feature formed by the light filtered by the light scattering”; Wherein when applied to the sonar sensor disclosed in Kim discloses the deactivation of the sonar when the object detection algorithm determines that while the host vehicle is driving, a another vehicle is driving towards the host vehicle, which constitutes the host vehicle driving forward and a preceding vehicle is driving backwards).
As per claim(s) 15, arguments made in rejecting claim(s) 6 are analogous.
As per claim(s) 16, arguments made in rejecting claim(s) 7 are analogous.
Claim(s) 8, and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim in view of Ng, Nix, Takahata, and Kim, Myong, and further in view of Gahlert et al. (Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images) hereinafter referenced as Gahlert.
Regarding claim 8, Kim in view of Ng, Nix, Takahata, and Kim, Myong discloses: The apparatus of claim 6.
Kim in view of Ng, Nix, Takahata, and Kim, Myong does not disclose expressly: wherein the object detection algorithm corresponds to a 3D object detection network configured to perform pre-learning such that the image of the area in front of the host vehicle is set to be input and that 3D positional information of the object present on the input image of the area in front of the host vehicle is output, and the controller is configured to obtain an angle between the traveling directions of the preceding vehicle and the host vehicle from an output of the object detection algorithm.
Gahlert discloses: a 3D object detection network (Gahlert: Abstract) configured to perform pre-learning such that the image of the area in front of the host vehicle is set to be input and that 3D positional information of the object present on the input image of the area in front of the host vehicle is output (Gahlert: Page 678: Col 2: Paragraph 2: “We evaluate our model using two challenging datasets: The KITTI Object Detection and Orientation Estimation dataset [1], [39]. This dataset contains 14 999 images that are divided into a training (7481 images) and a test set (7518 images) …As the labels for 3D bounding boxes in KITTI only contain labels for the yaw angle there are no annotations for both pitch and roll.”; Abstract: “we present an approach that predicts several key points selected from a virtual 3D bounding box around a vehicle instead of a pure 2D bounding box.”), and the controller is configured to obtain an angle between the traveling directions of the preceding vehicle and the host vehicle from an output of the object detection algorithm (Gahlert: Page 675: Col 2: Paragraph 1: “required information can be acquired for the autonomous vehicles by perceiving the physical movement of other vehicles and their orientation.”; Page 677: Col 2: Paragraph 1: “This set can be used to recalculate the 3D bounding box with vertices pi. To calculate the orientation of the object, the angles between two points pi can be utilized as shown in Fig. 3. The goal is to find the location in 3D space (δx, δy, δz) T, the dimension of the object (l, w, h) T as well as the rotational angles: yaw angle θ, pitch angle φ and the roll angle ψ.”; Wherein the yaw angle of the vehicle is based off the host vehicle’s camera, and therefore provides an angle between the traveling directions of the preceding and host vehicles).
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to substitute the object detection algorithm performed on the frontal image used for controlling the activation of the sonar based on detected vehicle orientation disclosed in Kim in view of Ng, Nix, Takahata, and Kim, Myong with the 3D bounding box detection algorithm taught by Gahlert. The suggestion/motivation for doing so would have been “it is crucial to gain a detailed knowledge about their current state at each point of time, such as one about the exact position of other vehicles in 3D space, their dimension – which can be important when overtaking e.g. a truck – as well as their orientation. The latter information is of main interest especially for the avoidance of collision e.g. when driving at high speed on a highway or complex junctions in inner-city driving scenarios…required information can be acquired for the autonomous vehicles by perceiving the physical movement of other vehicles and their orientation.” (Gahlert: Page 675: Col 2: Paragraph 1). Further, one skilled in the art could have substituted the elements as described above by known methods with no change in their respective functions, and the substitution would have yielded nothing more than predictable results. Therefore, it would have been obvious to combine Kim in view of Ng, Nix, Takahata, and Kim, Myong with Gahlert to obtain the invention as specified in claim 8.
As per claim(s) 17, arguments made in rejecting claim(s) 8 are analogous.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTHONY J RODRIGUEZ whose telephone number is (703)756-5821. The examiner can normally be reached Monday-Friday 10am-7pm.
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/ANTHONY J RODRIGUEZ/Examiner, Art Unit 2672
/SUMATI LEFKOWITZ/Supervisory Patent Examiner, Art Unit 2672