DETAILED ACTION
This action is filed in response to the application filed on 3/18/2024.
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 .
Information Disclosure Statement
Acknowledgement is made of Applicant’s Information Disclosure Statements (IDS) form PTO-1149 filed on 3/18/2024. This IDS has been considered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing mental steps without significantly more. Claim 1, and similarly Claim 11 recites the following abstract concepts in BOLD of:
An apparatus comprising:
a sensor; and
a processor,
wherein the processor is configured to:
determine contour points identified in a plane in a specific frame and included in an object box representing an object, wherein the plane is formed by a first axis and a second axis, the first axis corresponding to a moving direction of the sensor, and the second axis being perpendicular to the first axis;
determine whether a minimum value of first axis coordinates of the contour points is within a range, or whether a maximum value of the first axis coordinates of the contour points is within the range;
determine whether the object is absent in a first frame before the specific frame;
determine whether all or part of the contour points are unobstructed by another object different from the object;
determine whether a moved distance of the object between the specific frame and a second frame after the specific frame is greater than a specified distance, or whether a speed of the object is greater than a specified speed;
assign a reliability value to the object based on at least one of:
the minimum value of the first axis coordinates and the maximum value of the first axis coordinates being within the range,
the object being absent in the first frame,
all or part of the contour points being unobstructed by the another object,
the moved distance being greater than the specified distance, or
the speed being greater than the specified speed; and
determine, based on the reliability value, that the object is moving or movable; and
output a signal indicating that the object is moving or movable.
Under Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category as Claim 1 teaches an apparatus and Claim 11 teaches a method.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps. The steps of determining contour points, determining if those points fall in a specific range, determining if the object is absent in a first frame, determining if the points are unobstructed, determining if a moved distance or speed is greater than a threshold, and assigning a reliability value based on at least one of those prior determinations can all be interpreted as a mental process that can be performed in the human mind.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes the claimed methods and system are not tied to a particular machine or apparatus, and do not represent an improvement to another technology or technical field. The limitations describing the elements composing the device (i.e. a sensor and processor) merely indicate a field of use as it imposes no meaningful limitation of the claim. As recited in the MPEP 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a sensor and processor are generic computer elements and not considered significantly more than the abstract idea. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
The final limitation teaches outputting a signal indicating that the object is moving or movable, which is considered data outputting that does not integrate the abstract idea into a practical application. As recited in MPEP section 2106.05(g), displaying analysis/results is considered extra solution activity. See MPEP 2106.05(g) “Printing or downloading generated menus, Ameranth, 842 F.3d at 1241-42, 120 USPQ2d at 1854-55”, see also MPEP 2106.05(h), As a whole the claim itself is analogous to the Electric Power Group decision in which it was determined that “ Limiting the abstract idea of collecting information, analyzing it, and displaying certain results of the collection and analysis to data related to the electric power grid, because limiting application of the abstract idea to power-grid monitoring is simply an attempt to limit the use of the abstract idea to a particular technological environment, Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).”
Claims 2-10 and 12-20 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea:
Claims 2-5, 7-9, 12-15, and 17-19 further limit the abstract ideas of Claims 1 and 11 by disclosing additional mental processes of making mental determinations and assigning values based on those determinations. These additional abstract ideas are not practical applications that can be considered significantly more.
Claims 6, 10, 16, and 20 recite necessary data gathering and does not integrate the abstract idea into a practical application. The limitation amounts to necessary data gathering and outputting. See Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015) (presenting offers and gathering statistics amounted to mere data gathering).
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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Iketani (US20080166024) in view of Bobbitt(US9594963B2) and Lages (US20060115113A1)
Regarding Claims 1 and 11, Iketani teaches, An apparatus and method comprising: a sensor (e.g. see [Fig. 1] Element 113 a speed sensor, Element 114 a yaw rate sensor, Element 112 a camera, and Element 111 a laser radar); and
a processor (e.g.see [0065] “The obstacle detecting device 115 is configured, for example, by a CPU (Central Processing Unit), ROM (Read Only Memory), RAM (Random Access Memory)”),
wherein the processor is configured to: determine contour points identified in a plane in a specific frame and included in an object box representing an object (e.g. see [0154] “FIG. 18 shows an example for the case in which the feature points of the forward images P11 and P12 are extracted based only on the feature amount, and FIG. 19 shows an example for the case in which the feature points of the same forward images P11 and 212 are extracted using the above-described feature point extraction process. Incidentally, the black circles in the forward images P11 and P12 represent the feature points extracted”), wherein the plane is formed by a first axis and a second axis, the first axis corresponding to a moving direction of the sensor, and the second axis being perpendicular to the first axis (e.g. see [0058] “in the radar coordinate system, a beam emitting port of the laser radar Ill corresponds to a point of origin; a distance direction (front-to-back direction) of the automotive vehicle corresponds to the z-axis direction; the height direction perpendicular to the z-axis direction corresponds to the y-axis direction; and the transversal direction (left-to-right direction) of the automotive vehicle perpendicular to the z- and y-axis directions corresponds to the x-axis direction”);
determine whether a minimum value of first axis coordinates of the contour points is within a range, or whether a maximum value of the first axis coordinates of the contour points is within the range (e.g. see [0095-0096] “Specifically, the position determining portion 151 narrows down the process subject by extracting the objects that satisfy the following expression (3) based on the position (X, Z) of the objects detected by the laser radar 111. |X|<Xth and Z<Zth (3). In the expression (3), Xth and Zth are predetermined threshold values. Therefore, if the vehicle 301 shown in FIG. 7 is the automotive vehicle, objects present within a detection region Rth having a width of Xth and a length of Zth in the forward area of the vehicle 301 are extracted”).
determine whether a moved distance of the object between the specific frame and a second frame after the specific frame is greater than a specified distance (e.g. see [0171] “the movement vector at the select feature point is determined as being the moving object vector when the magnitude of the x-axis directional component of the transformation vector is greater than that of the right-hand side of the expression (12)”), or whether a speed of the object is greater than a specified speed (e.g. see [0105] “With this, as shown in FIG. 8, among objects present within the detection region, the objects whose speed in the distance direction of the automotive vehicle is greater than a predetermined threshold value, such as preceding vehicles or opposing vehicles, are excluded from the process subject”);
assign a reliability value to the object based on at least one of: the minimum value of the first axis coordinates and the maximum value of the first axis coordinates being within the range,
the object being absent in the first frame, all or part of the contour points being unobstructed by the another object, the moved distance being greater than the specified distance, or
the speed being greater than the specified speed (e.g. see [0171] “the movement vector at the select feature point is determined as being the moving object vector when the magnitude of the x-axis directional component of the transformation vector is greater than that of the right-hand side of the expression (12)(i.e. a specified distance),” and [0175] “the object classifying portion 263 determines the objects within the select ROI as being the moving object when the ratio of the moving object vectors to the entire movement vectors within the select ROT is equal to or greater than a predetermined threshold value, for example,” Examiner notes the ratio being at or above a threshold is the reliability value)), and
determine, based on the reliability value, that the object is moving or movable (e.g. see [0175] “ROI based on the classification results of the movement vectors within the select ROI. For example, the object classifying portion 263 determines the objects within the select ROI as being the moving object when the number of moving object vectors within the select ROI is equal to or greater than a predetermined threshold value”); and
output a signal indicating that the object is moving or movable (e.g. see [0190] “In step S11, the output portion 133 supplies the detection results”).
Iketani does not explicitly disclose determin[ing] whether the object is absent in a first frame before the specific frame; determin[ing] whether all or part of the contour points are unobstructed by another object different from the object.
In the same field of endeavor, Bobbitt teaches determin[ing] whether the object is absent in a first frame before the specific frame (e.g. see [Col 2 lines 26-36] “Accordingly, segments of the input video data comprising consecutive video frames assigned the object absent label and the static label are classified as “no object present” segments; segments comprising consecutive video frames that are each assigned the object present label and the motion present label are classified as “object present and in transition” segments; and segments of the input video data comprising consecutive video frames that are each assigned the object present label and the static label are classified as “object present and stopped” segments,” and [Col 7 lines 57-62] “More particularly, appearance features are extracted from the frames and accumulated over time for each of the motion state segments at 114, from a first frame in each segment up to the point in time at which the classification and labeling decision is made at 112 to transition to a next, different segment”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the object recognition device of Iketani with the object absent frame determinations of Bobbitt for the purpose of determining movement of an object with the advantage of locating where the movement started.
Iketani as modified by Bobbitt does not explicitly disclose determin[ing] whether all or part of the contour points are unobstructed by another object different from the object. In the same field of endeavor, Lages teaches determin[ing] whether all or part of the contour points are unobstructed by another object different from the object (e.g. see [0004] “Furthermore, it can be necessary to deal with complicated methods for a treatment of effects such as an object decay, i.e. the decay of an object into two objects. An object decay of this kind can in particular arise when partial hiding of a real object, which is for example completely detected in preceding cycles, takes place through real objects in the foreground and thus two separate objects apparently arise,” and [0052] “it is preferred to carry out a hiding recognition, during the association of segments to objects. Through a hiding recognition a determination can be made with greater security whether two segments which are to be associated with one object could have arisen by partial covering over of the corresponding real object, so that it is possible to recognize more simply whether the two segments should be associated with one object or whether currently an object decay is present corresponding to the real circumstances”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the object recognition device of Iketani with the obstruction determinations of Lages for the purpose of determining the presence of an object with the advantage of determining how much of the object is present in the frame in order to evaluate the accuracy of the object recognition.
Regarding Claims 2 and 12, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein the processor is configured to: determine whether the minimum value of the first axis coordinates is within the range; and determine whether the maximum value of the first axis coordinates is within the range based on a determination that the minimum value of the first axis coordinates is within the range (e.g. see [0109-0112] “When the position of the central point OC12 in the radar coordinate system is expressed by (X2, Y2, Z2), X2 and Z2 are calculated from the object information supplied from the laser radar 111, and Y2 is calculated from the height of the position at which the laser radar 111 is installed, from the ground level. Then, a region 324 having a height of 2 A (m) and a width of 2 B (m), centered on the central point OC12 is set as the ROI of the object 323 The position of the ROI for each of the objects extracted by the object extracting portion 141 is transformed from the position in the radar coordinate system into the position in the forward image, based on the following relational expressions (6) to (8). [ XL YL ZL ] = R [ Xc Yc Zc ] + T ( 6 ) Xp = X 0 + F dXp Xc Zc ( 7 ) Yp = Y 0 + F dYp Yc Zc ( 8 ) ##EQU00002## [0111] In the expressions, (XL, YL, ZL) represents coordinates in the radar coordinate system; (Xc, Yc, Zc) represents coordinates in the camera coordinate system; and (Xp, Yp) represents coordinates in the coordinate system of the forward image. In the coordinate system of the forward image, the center (X0, Y0) of the forward image set by a well-known calibration method corresponds to a point of origin; the horizontal direction corresponds to the x-axis direction; the vertical direction corresponds to the y-axis direction; the right direction corresponds to the positive direction of the x-axis direction; and the upward direction corresponds to the positive direction of the y-axis direction. Incidentally, R represents a 3-by-3 matrix; and T represents a 3-by-1 matrix, both of which are set by a well-known camera calibration method. Incidentally, F represents a focal length of the camera 112; dXp represents a horizontal length of one pixel of the forward image; and dYp represents a vertical length of one pixel of the forward image. [0112] With this, ROIs are set in the forward image for each of the extracted objects, the ROIs including the entire or a portion of the object and having a size corresponding the distance to the object,”).
Regarding Claims 3 and 13, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein the processor is configured to:
assign, to the object, a first boundary object feature value that is a specified value to the object in the specific frame, based on the minimum value of the first axis coordinates being within the range (e.g. see [0009] “one aspect of the present invention includes a feature amount calculating means for calculating a feature amount of pixels within a region of an object detected by a radar, the region including the entire or a portion of the object and being a designated region in an image,” and [0118] “the feature amount calculating portion 162 calculates the intensity at the corner of the image within the select ROI as the feature amount based on a predetermined technique (for example, the Harris corner detection method). The feature amount calculating portion 162 supplies information representing the position of the select ROI in the forward image and the feature amount of the pixels within the select ROI to the feature point extracting portion 163”);
assign, to the object, the second boundary object feature value assigned to the object in the first frame based on: the assigned first boundary object feature value being the specified value, the object existing in the first frame (e.g. see [0137] “FIG. 12 shows an example of the feature amount of each pixel within the ROI. Each square column within the ROI 351 shown in FIG. 12 represents a pixel, and a feature amount of the pixel is described within the pixel. The coordinates of each pixel within the ROI 351 are represented by a coordinate system in which the pixel at the top left corner of the ROI 351 is a point of origin (0, 0); the horizontal direction is the x-axis direction; and the vertical direction is the y-axis direction”), or the maximum value of the first axis coordinates not falling within the range; and
assign the reliability value to the object included in the specific frame, based on at least one of: the first boundary object feature value assigned to the object in the specific frame and the second boundary object feature value assigned to the object in the specific frame being the specified value, all or part of contour points being unobstructed by another object, the moved distance being greater than the specified distance ((e.g. see [0171] “the movement vector at the select feature point is determined as being the moving object vector when the magnitude of the x-axis directional component of the transformation vector is greater than that of the right-hand side of the expression (12)(i.e. a specified distance),” and [0175] “the object classifying portion 263 determines the objects within the select ROI as being the moving object when the ratio of the moving object vectors to the entire movement vectors within the select ROT is equal to or greater than a predetermined threshold value, for example,” Examiner notes the ratio being at or above a threshold is the reliability value),or the speed of the object being greater than a specified speed.
Iketani does not explicitly disclose assign, to the object, a second boundary object feature value that is the specified value to the object in the second frame, based on the object being absent in the first frame and the maximum value of the first axis coordinates being within the range.
In the same field of endeavor, Bobbitt teaches assign, to the object, a second boundary object feature value that is the specified value to the object in the second frame, based on the object being absent in the first frame (e.g. see [Col 8 line 54-Col. 9 line 4 ] “FIG. 3 graphically plots two examples of the determined BGS feature values and Frame-Diff features over a common (horizontal axis) timeline 306 for train presence events at the station platform of FIG. 2 for two idealized situations, wherein no noise impacts the respective values. The upper graph plotline 302 represents values of both of the determined BGS feature values and Frame-Diff features with respect to the case of a passing train (or briefly stopped train), wherein both values are low at 312 before the train enters the station, upon entry of the train into the station at 314 increase and remain steady at a high level 316, then drop again to a low level 318 when the train leaves the station at 315. An ideal moving train passing through the ROI without stopping should consistently impose large level of BGS area values and frame-differencing mask feature values at 316 which meet the respective thresholds at 108 and 110 of FIG. 1, and thus result in classifying a video segment comprising these frames as “train present and in transition” at 112”) and the maximum value of the first axis coordinates being within the range (e.g. see [Col. 6 lines 43-57] “More particularly, the ROI 202 is generally defined at 102 (FIG. 1) to encompass an area within the field of view 204 wherein the only moving objects distinguishable from background (static) objects in the image should be train cars, thereby avoid false alarms that may be generated by the movement of pedestrians or other, non-train car object movements. However, it will be understood that some embodiments may not limit the ROI to train tracks, but may include other areas, and use other processes to distinguish train car motion features for the motion features of other objects. ROI's with shapes other than polygonal may also be practiced (for example, round, oval, conical, etc.). Further, some embodiments may focus the entire field of view 204 on only a track area, and thus the entire field of view 204 may be the ROI for the image data”).
It would have been obvious to one of ordinary skill in the art before the effective filling date to combine the object recognition device of Iketani with the object absent frame determinations of Bobbitt for the purpose of determining movement of an object with the advantage of locating where the movement started.
Regarding Claim 4 and 14, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein the processor is configured to: determine a relative moved distance of the object with respect to a vehicle based on a difference between a position of a point corresponding to the object in the specific frame and a position of a point corresponding to the object in the second frame (e.g. see [0167] “the direction and magnitude of the movement vector (hereinafter referred to as a background vector) of the pixels on a stationary object within the forward image, that is, the direction and magnitude of the movement vector generated by only the movement in the distance direction of the automotive vehicle can be calculated based on the position of the pixels in the forward image, the distance of the stationary object from the automotive vehicle, and the distance that the automotive vehicle has traveled within the time between two frames of the forward image used in detection of the movement vector”);
determine the moved distance based on a sum of a moved distance of the vehicle and the relative moved distance of the object (e.g. see [0171] “in the expression, x represents the distance (length) of the select feature point in the x-axis direction from the central point (X0, Y0) of the forward image; t.sub.Z represents the distance that the automotive vehicle has traveled within the time between the two frames of the forward image used in the detection of the movement vector; and Z represents the distance of the object within the select ROI from the automotive vehicle… when the directions in the x-axis direction of the transformation vector at the select feature point and the theoretical background vector are the same, the movement vector at the select feature point is determined as being the moving object vector when the magnitude of the x-axis directional component of the transformation vector is greater than that of the right-hand side of the expression (12),”); and
match the point corresponding to the object in the specific frame to the point corresponding to the object in the second frame (e.g. see [0159] “For example, the vector detecting portion 164 detects pixels within the forward image of the subsequent frame corresponding to the feature points within the select ROI so that a vector directed from each feature point to the detected pixel is detected as the movement vector at each feature point”).
Regarding Claims 5 and 15, Iketani, Bobbitt, and Lages teach the limitations of Claims 4 and 14. Iketani further discloses wherein the processor is configured to: determine a center point contained in a most preceding line segment in a moving direction of the object among line segments constituting the object box as a point corresponding to the object (e.g. see [Fig. 9] and [0109] “Next, the central point OC12 of a rectangular region OR12 having substantially the same width and height as the grouped beams BM12-1 to BM12-3 is determined as the central point of the object 323. When the position of the central point OC12 in the radar coordinate system is expressed by (X2, Y2, Z2), X2 and Z2 are calculated from the object information supplied from the laser radar 111, and Y2 is calculated from the height of the position at which the laser radar 111 is installed, from the ground level. Then, a region 324 having a height of 2 A (m) and a width of 2 B (m), centered on the central point OC12 is set as the ROI of the object 323”), or determine another point contained in the most preceding line segment as the point corresponding to the object.
Regarding Claim 6 and 16, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein values, of the first axis coordinates of points included in the range, are included between: a value of a first axis coordinate of a point corresponding to a vehicle and
a value of a first axis coordinate separated from the first axis coordinate of the point corresponding to the vehicle by a threshold distance in a direction of the first axis (e.g. see [0095-0096] “in step S31, the position determining portion 151 narrows down the process subject based on the position of the objects. Specifically, the position determining portion 151 narrows down the process subject by extracting the objects that satisfy the following expression (3) based on the position (X, Z) of the objects detected by the laser radar 111. |X|<Xth and Z<Zth (3). In the expression (3), Xth and Zth are predetermined threshold values. Therefore, if the vehicle 301 shown in FIG. 7 is the automotive vehicle, objects present within a detection region Rth having a width of Xth and a length of Zth in the forward area of the vehicle 301 are extracted”).
Regarding Claim 7 and 17, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein the processor is configured to: determine that a second axis coordinate of a point corresponding to the object is within a second axis range (e.g. see [0095-0096] “in step S31, the position determining portion 151 narrows down the process subject based on the position of the objects. Specifically, the position determining portion 151 narrows down the process subject by extracting the objects that satisfy the following expression (3) based on the position (X, Z) of the objects detected by the laser radar 111. |X|<Xth and Z<Zth (3). In the expression (3), Xth and Zth are predetermined threshold values. Therefore, if the vehicle 301 shown in FIG. 7 is the automotive vehicle, objects present within a detection region Rth having a width of Xth and a length of Zth in the forward area of the vehicle 301 are extracted”); and
assign the reliability value to the object based on at least one of: the second axis coordinate being within the second axis range, at least one of the minimum value of the first axis coordinates and the maximum value of the first axis coordinates being within the range, the object being absent in the first frame, all or part of the contour points being unobstructed by the another object, the moved distance being greater than the specified distance (e.g. see [0171] “the movement vector at the select feature point is determined as being the moving object vector when the magnitude of the x-axis directional component of the transformation vector is greater than that of the right-hand side of the expression (12)(i.e. a specified distance),” and [0175] “the object classifying portion 263 determines the objects within the select ROI as being the moving object when the ratio of the moving object vectors to the entire movement vectors within the select ROT is equal to or greater than a predetermined threshold value, for example,” Examiner notes the ratio being at or above a threshold is the reliability value), or the speed of the object being greater than the specified speed.
Regarding Claims 8 and 18, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein the processor is configured to: determine whether a second axis coordinate of a point corresponding to the object is located in a second axis range including a specified number of lanes on both sides of a lane where a vehicle is located (e.g. see [0066] “FIG. 2 is a bird's-eye view showing an example of the detection results of the laser radar 111. In the drawing, the distance represents a distance from the automotive vehicle; and among four vertical lines, the inner two lines represent a vehicle width of the automotive vehicle and the outer two lines represent a lane width of the lanes along which the automotive vehicle travels. In the example of FIG. 2, an object 201 is detected within the lanes on the right side of the automotive vehicle and at a distance greater than 20 meters from the automotive vehicle, and additionally, another objects 202 and 203 are detected off the lanes on the left side of the automotive vehicle and respectively at a distance greater than 30 meters and at a distance of 40 meters, from the automotive vehicle,”); and
assign the reliability value to the object based on the second axis coordinate of the point corresponding to the object being located in the second axis range (e.g. see [0094] “In step S5, the obstacle detecting device 115 executes an ROI setting process,” and [0097] “the threshold value Xth is set to a value obtained by adding a predetermined length as a margin to the vehicle width (a width Xc of the vehicle 301 in FIG. 7) or to the lane width the lanes along which the automotive vehicle travels,” and [0175] “The object classifying portion 263 detects the type of the objects within the select ROI based on the classification results of the movement vectors within the select ROI…the object classifying portion 263 determines the objects within the select ROI as being the moving object when the ratio of the moving object vectors to the entire movement vectors within the select ROT is equal to or greater than a predetermined threshold value,” Examiner notes the reliability value is developed as a result of the region of interest set in accordance with the lane distances).
Regarding Claims 9 and 19, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein the processor is configured to assign, to the object, an identifier indicating that the object is moving or movable, based on determining that the object is moving or movable (e.g. see [0182-0183] “In step S77, the moving object classifying portion 264 detects the type of the moving object, and the clustering process is completed. Specifically, the object classifying portion 263 supplies information representing the position of the select ROI in the forward image to the moving object classifying portion 264. The moving object classifying portion 264 detects whether the moving object, which is the object within the select ROI, is a vehicle, using a predetermined image recognition technique, for example… In this way, since the detection subject is narrowed down to the moving object and it is detected whether the narrowed-down detection subject is the vehicle traveling in the transversal direction of the automotive vehicle, it is possible to improve the detection precision”).
Regarding Claims 10 and 20, Iketani, Bobbitt, and Lages teach the limitations of Claims 1 and 11. Iketani further discloses wherein the specified speed indicates a threshold value for determining whether the object is moving or movable (e.g. see [0103-0105] “In step S32, the speed determining portion 152 narrows down the process subject based on the speed of objects. Specifically, the speed determining portion 152 narrows down the process subject by extracting, from the objects extracted by the position determining portion 151, objects that satisfy the following expression (5). Vv(t)+dZ(t)≤ ϵ (5).In the expression, Vv(t) represents the speed of the automotive vehicle at a time point t, and dZ(t) represents a relative speed of the object at a time point t in the z-axis direction (distance direction) with respect to the automotive vehicle. Incidentally, .epsilon. is a predetermined threshold value”),
based on a minimum first axis coordinate value of the object or a maximum first axis coordinate value of the object being included in the range, and wherein values of the first axis coordinates of points included in the range are included between: a value of a first axis coordinate of a point corresponding to a vehicle and a value of a second axis coordinate separated from the first axis coordinate of the point corresponding to the vehicle by a threshold distance in a direction of the first axis (e.g. see [0095-0096] “in step S31, the position determining portion 151 narrows down the process subject based on the position of the objects. Specifically, the position determining portion 151 narrows down the process subject by extracting the objects that satisfy the following expression (3) based on the position (X, Z) of the objects detected by the laser radar 111. |X|<Xth and Z<Zth (3). In the expression (3), Xth and Zth are predetermined threshold values. Therefore, if the vehicle 301 shown in FIG. 7 is the automotive vehicle, objects present within a detection region Rth having a width of Xth and a length of Zth in the forward area of the vehicle 301 are extracted”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
WO 2012070474 A1 teaches object recognition by determining contour points, and assigning a reliability value.
KR 20210139181 A teaches LIDAR object recognition including determining the boundaries of physical objects in the vicinity of the vehicle and determining if an object is present.
JP 4020982 B2 teaches tracking including the stationary state of the moving object even if inter-frame difference processing is used by detecting and evaluating the feature amount of the moving object.
JP 2007502473 A teaches systems and methods for recognition, classification, and spatial localization of bounded three-dimensional objects. In particular, it relates to computerized methods for object recognition, classification and localization.
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/NYLA GAVIA/Examiner, Art Unit 2857
/Catherine T. Rastovski/Supervisory Primary Examiner, Art Unit 2857
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