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 .
Status of Claims
Claims 1-19 are currently pending and have been examined
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The application claims benefit to the foreign priority date of 09 May 2023.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 30 October 2025 and 06 April 2024 have been considered by the examiner, and an initialed copy of the IDS is hereby attached.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Tsou et al. (US 5508706 A) in view of Shin et al. (US 11312371 B2) in view of MathWorks Inc., “Kernel Distribution”, MATLAB, 2013, Kernel Distribution - MATLAB & Simulink,
Regarding Claim 1,
Tsou discloses a target detection device comprising:
a histogram processor (Col 18, lines 63-67 “microprocessor” of Tsou) configured to create and update a histogram representing an object detection frequency for each range using radar reception signals in response to a movement of a host vehicle; (Refer to Fig 14a, Element 100; Col 11 Line 64-65 “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 16 Line28-33; “The 2-D range-speed histogram is composed of a plurality of quantized bins which accumulate or count occurrences of samples having a value within each quantized bin. The count thus accumulated is hereinafter referred to as a "vote" for a quantized bin.” of Tsou)
a target determiner configured to determine a target range having a value equal to or greater than a threshold value, and determine an object included in the target range as a target (Col 15 Line 21-32 “The target decision device 500, connected to the 2-D parameter estimation device 504, evaluates the 2-D estimation signal and determines whether a valid target exists. Only targets greater than the threshold range profile signal are analyzed using the Hough transform, and only the target paths that correlate closely enough to constant velocity motion pass the thresholding in 2-D Hough space. A target is valid if it persists over enough thresholding intervals, and if its path is correlated to a relatively non-accelerating path. If the target decision device 500 determines that a valid target exists, the target decision device 500 ends the acquisition mode and initiates the tracking mode.” Of Tsou)
(Col 15 Line 59-62; “As the range and speed data generated by the Hough transform is collected in the bins of the 2-D range-speed histogram, peaks are formed in the range-speed bins which identify a target range and speed at 510 in FIG. 27.” of Tsou)
Tsou does not disclose:
a candidate area determiner configured to determine a candidate area within a specific distance range based on the histogram; and
a target determiner configured to....determine an object included in the target range as a stationary target.
Shin teaches the concept of:
a candidate area determiner configured to determine a candidate area within a specific distance range based on detection data for a range based on a movement of a host vehicle;
(Col 7 Line 16-18; “The feature point extraction module 132 may generate a predetermined region in the detection region of the radar as a region of interest (ROI).” of Shin)
(Col 7 Line 50-57; “detection data of the stationary object is present in the left region of interest 31 moved to the left, the feature point extraction module 132 may extract the feature points 40 from the left region of interest 31. Similarly, since the detection data of the stationary object exists in the right region of interest 51 moved to the right, the feature point extraction module 132 may extract the feature points 60 from the right region of interest 51.” of Shin)
(Col 7 Line 7-9; “The feature point extraction module 132 may correct the movement amount of the vehicle when accumulating the detection data.” of Shin)
(Col 7 Line 12-15; “the feature point extraction module 132 may receive driving information of the vehicle from the driving information sensor and obtain the movement amount of the vehicle.” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify histogram as taught by Tsou to include determining a candidate area as taught by Shin since the histogram of Tsou is based off of radar detection data of a moving vehicle and utilizes distance ranges. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou in order to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Shin teaches:
a target determiner configured to....determine an object included in the target range as a stationary target. (Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the target determiner detecting an object as disclosed by Tsou to be a stationary target as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Tsou and Shin do not teach:
a kernel density estimator configured to create a kernel function for each of a plurality of range data included in the candidate area and determine an accumulated probability density by accumulating a plurality of kernel functions; and
a target determiner configured to determine a target range having an accumulated probability density equal to or greater than a threshold probability density;
MathWorks teaches:
a kernel density estimator configured to create a kernel function for each of a plurality of range data included in the candidate area and determine an accumulated probability density by accumulating a plurality of kernel functions; and (Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
and (Fourth Paragraph of Kernel Smoothing Function “Alternatively, the kernel distribution builds the pdf by creating an individual probability density curve for each data value, then summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set.” of MathWorks)
Tsou and MathWorks are analogous since both references are directed utilizing histogram data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the histogram, and the target range having a value equal to or greater than a threshold value as taught by Tsou to utilize a kernel density function on each bin of a histogram in order to determine an accumulated probability density as taught by MathWorks, which can then be utilized in the determination of the target range. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou in order to avoid making assumptions about the distribution of the data. (Overview of MathWorks)
Regarding Claim 2, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 1.
Tsou discloses,
wherein the histogram processor updates the histogram by accumulating or clustering object detection frequencies within a driving path of the host vehicle at a plurality of time points. (Refer to Fig 14a, Element 100; Col 11 Line 64-65 “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 16 Line 24-36; “The Hough transform maps points in 2-D x (range) and y (time) space to lines in 2-D slope and intercept space. Each point in the x-y space generates a line in slope-intercept space. Speed or range rate can be derived from the slope value and range can be derived from the intercept value. The 2-D range-speed histogram is composed of a plurality of quantized bins which accumulate or count occurrences of samples having a value within each quantized bin. The count thus accumulated is hereinafter referred to as a "vote" for a quantized bin. The 2-D range-speed histogram including the plurality of quantized bins containing votes approximates a probability density function for speed and range from many points in the x-y space using the Hough transform.” of Tsou)
Tsou does not disclose the limitation below, but Shin teaches,
wherein the histogram processor updates...object detection frequencies for stationary objects
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the device by Tsou to include a stationary target as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Regarding Claim 3, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 2.
Tsou does not disclose the following limitation. However,
Shin discloses,
wherein the candidate area determiner determines an area having an object detection frequency greater than or equal to a threshold frequency as the candidate area.
(Col 7 Line 16-18; “The feature point extraction module 132 may generate a predetermined region in the detection region of the radar as a region of interest (ROI).” of Shin)
(Col 7 Line 50-57; “detection data of the stationary object is present in the left region of interest 31 moved to the left, the feature point extraction module 132 may extract the feature points 40 from the left region of interest 31. Similarly, since the detection data of the stationary object exists in the right region of interest 51 moved to the right, the feature point extraction module 132 may extract the feature points 60 from the right region of interest 51.” of Shin)
(Col 7 Line 58-67 “the feature point extraction module 132 may be configured to extract feature points in the case that the number of detection data for the object existing in the region of interest is greater than or equal to a predetermined number. The predetermined number as the reference for the feature point extraction may be adjusted according to the size of the region of interest. According to an example, a point having the highest density of detection data in the region of interest may be extracted as the feature point.” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the device as taught by Tsou to include a candidate area to determine a detection frequency threshold based off of detection data as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Tsou in view of Shin does not disclose the following limitation. However, MathWorks discloses,
wherein the candidate area determiner determines an area including at least one range bin
(Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
Tsou, Shin and MathWorks are analogous as all disclose a data process and analysis workflow to identify and track radar reception signals that determine the distance of a target object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process of determining a target as disclosed by Tsou to include determining detection of histogram data as disclosed by MathWorks. One of ordinary skill in the art before the effective the effective filing date of the claimed invention would have been motivated to modify Tsou to accurately describe the data (Overview of MathWorks).
Regarding Claim 4, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 1.
Tsou discloses,
wherein the target determiner determines a range with the maximum accumulated probability density among one or more ranges with the accumulated probability density equal to or greater than the threshold value as the target range.
(Col 15 Line 21-25; “The target decision device 500, connected to the 2-D parameter estimation device 504, evaluates the 2-D estimation signal and determines whether a valid target exists. Only targets greater than the threshold range profile signal are analyzed using the Hough transform,” of Tsou) and
(Col 15 Line 59-62; “As the range and speed data generated by the Hough transform is collected in the bins of the 2-D range-speed histogram, peaks are formed in the range-speed bins which identify a target range and speed at 510 in FIG. 27.” of Tsou)
Regarding Claim 5, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 1.
Tsou discloses,
wherein the target determiner determines an object included in a range having the accumulated probability density less than or equal to the threshold probability density as a clutter. (Col 16 Line 4-10; “Range profiles for a range bin should remain above the corresponding range bin in the threshold range profile signal for a majority of the duration of the Hough transform to be considered as authentic targets. Requiring range profiles to remain above the threshold range profile signal provides further discrimination of spurious data, clutter and noise signals which can trigger false alarms.” of Tsou)
Regarding Claim 6, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 1.
Tsou discloses,
wherein the target determiner provides information... to a driver assistance system module for following a preceding vehicle included in the host vehicle. (Refer to Fig 14a, Element 100; and Col 11 Line 64-65; “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 12 Line 22-23 “The radar system 100 mounted on the vehicle 300, such as an automobile, can be used in intelligent cruise control systems, collision avoidance systems,” of Tsou)
(Col 12 Line 56-59 “The RSP 380 can be connected with an input and display device 384, a controller 386 and a memory 388 (which can include RAM, ROM etc.) via an interface 390,” of Tsou)
(Col 15 Line 35-37 “The controller 386 can determine whether a valid target exists and trigger the target decision device 500 into the tracking mode.” of Tsou)
Tsou does not disclose the limitation below, but Shin teaches,
wherein the target determiner provides information about the stationary target (Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the device as taught by Tsou to be a stationary target as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Regarding Claim 7,
Tsou discloses a target detection method comprising:
creating and updating a histogram representing an object detection frequency for each range using radar reception signals in response to a movement of a host vehicle; (Refer to Fig 14a, Element 100; Col 11 Line 64-65 “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 16 Line28-33; “The 2-D range-speed histogram is composed of a plurality of quantized bins which accumulate or count occurrences of samples having a value within each quantized bin. The count thus accumulated is hereinafter referred to as a "vote" for a quantized bin.” of Tsou)
determining a target range having a value equal to or greater than a threshold value, and determining an object included in the target range (Col 15 Line 21-29; “The target decision device 500, connected to the 2-D parameter estimation device 504, evaluates the 2-D estimation signal and determines whether a valid target exists. Only targets greater than the threshold range profile signal are analyzed using the Hough transform, and only the target paths that correlate closely enough to constant velocity motion pass the thresholding in 2-D Hough space. A target is valid if it persists over enough thresholding intervals, and if its path is correlated to a relatively non-accelerating path.” of Tsou)
(Col 15 Line 59-62; “As the range and speed data generated by the Hough transform is collected in the bins of the 2-D range-speed histogram, peaks are formed in the range-speed bins which identify a target range and speed at 510 in FIG. 27.” of Tsou)
Tsou does not disclose:
determining a candidate area within a specific distance range based on the detection data; and
determining an object included in the target range as a stationary target.
Shin teaches the concept of:
determining a candidate area within a specific distance range based on the detection data;
(Col 7 Line 16-18; “The feature point extraction module 132 may generate a predetermined region in the detection region of the radar as a region of interest (ROI).” of Shin)
(Col 7 Line 58-67 “the feature point extraction module 132 may be configured to extract feature points in the case that the number of detection data for the object existing in the region of interest is greater than or equal to a predetermined number. The predetermined number as the reference for the feature point extraction may be adjusted according to the size of the region of interest. According to an example, a point having the highest density of detection data in the region of interest may be extracted as the feature point.” of Shin)
Shin teaches:
determining an object included in the target range as a stationary target.
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the target range having a value equal to or greater than a threshold value as disclosed by Tsou to be a stationary target as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Tsou and Shin do not teach:
Determining a candidate area... based on the histogram
creating a kernel function for each of a plurality of range data included in the candidate area, and determining an accumulated probability density by accumulating a plurality of kernel functions; and
determining a target range having an accumulated probability density equal to or greater than a threshold probability density,
MathWorks teaches,
Determining a candidate area... based on the histogram
(Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
creating a kernel function for each of a plurality of range data included in the candidate area, and determining an accumulated probability density by accumulating a plurality of kernel functions; and (Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
(Fourth Paragraph of Kernel Smoothing Function “Alternatively, the kernel distribution builds the pdf by creating an individual probability density curve for each data value, then summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set.” of MathWorks)
determining a target range having an accumulated probability density equal to or greater than a threshold probability density,
(Fourth Paragraph of Kernel Smoothing Function “Alternatively, the kernel distribution builds the pdf by creating an individual probability density curve for each data value, then summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set.” of MathWorks)
Tsou, Shin and MathWorks are analogous as all disclose a data process and analysis workflow to identify and track radar reception signals that determine the distance of a target object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the target range having a value equal to or greater than a threshold value as taught by Tsou to include the kernel probability density function on each target range as taught by MathWorks. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou in order to accurately describe the distribution of data. (Overview of MathWorks)
Regarding Claim 8, Tsou in view of Shin, in further view of MathWorks discloses the method of Claim 7.
Tsou discloses,
wherein creating and updating a histogram comprises updating the histogram by accumulating or clustering object detection frequencies within a driving path of the host vehicle at a plurality of time points. (Col 16 Line 28-31; “The 2-D range-speed histogram is composed of a plurality of quantized bins which accumulate or count occurrences of samples having a value within each quantized bin. The count thus accumulated is hereinafter referred to as a "vote" for a quantized bin.” of Tsou)
(Col 16 Line 24-27; “The Hough transform maps points in 2-D x (range) and y (time) space to lines in 2-D slope and intercept space.” of Tsou)
(Refer to Fig 14a, Element 100; Col 11 Line 64-65 “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
Tsou does not disclose the limitation below, but Shin teaches,
wherein creating and updating a histogram comprises... object detection frequencies for stationary objects
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process as taught by Tsou to be of stationary targets as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Regarding Claim 9, Tsou in view of Shin, in further view of MathWorks discloses the method of Claim 8.
Tsou does not disclose the limitation below, but Shin teaches,
wherein determining a candidate area comprises determining an area having an object detection frequency greater than or equal to a threshold frequency as the candidate area.
(Col 6 Line 30-33; “The stationary object determination module 131 may determine the stationary object that is a target object for extracting the feature point among objects detected by the radar.” of Shin)
(Col 7 Line 16-18; “The feature point extraction module 132 may generate a predetermined region in the detection region of the radar as a region of interest (ROI).” of Shin)
(Col 7 Line 50-57; “detection data of the stationary object is present in the left region of interest 31 moved to the left, the feature point extraction module 132 may extract the feature points 40 from the left region of interest 31. Similarly, since the detection data of the stationary object exists in the right region of interest 51 moved to the right, the feature point extraction module 132 may extract the feature points 60 from the right region of interest 51.” of Shin)
(Col 7 Line 58-67 “the feature point extraction module 132 may be configured to extract feature points in the case that the number of detection data for the object existing in the region of interest is greater than or equal to a predetermined number. The predetermined number as the reference for the feature point extraction may be adjusted according to the size of the region of interest. According to an example, a point having the highest density of detection data in the region of interest may be extracted as the feature point.” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process of determining a target as taught by Tsou to include a candidate area to determine a detection frequency threshold based off of detection data as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Tsou in view of Shin does not disclose the following limitation. However, MathWorks discloses,
wherein the candidate area comprises... including at least one range bin
(Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
Tsou, Shin and MathWorks are analogous as all disclose a data process and analysis workflow to identify and track radar reception signals that determine the distance of a target object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process of determining a target as disclosed by Tsou to include determining detection of histogram data as disclosed by MathWorks. One of ordinary skill in the art before the effective the effective filing date of the claimed invention would have been motivated to modify Tsou to properly describe the data (Overview of MathWorks).
Regarding Claim 10, Tsou in view of Shin, in further view of MathWorks discloses the method of Claim 7.
Tsou discloses,
wherein determining a target range comprises determining a target range with the maximum accumulated probability density among one or more ranges with the accumulated probability density equal to or greater than the threshold value as the target range. (Col 15 Line 21-25; “The target decision device 500, connected to the 2-D parameter estimation device 504, evaluates the 2-D estimation signal and determines whether a valid target exists. Only targets greater than the threshold range profile signal are analyzed using the Hough transform,” of Tsou) and
(Col 15 Line 59-62; “As the range and speed data generated by the Hough transform is collected in the bins of the 2-D range-speed histogram, peaks are formed in the range-speed bins which identify a target range and speed at 510 in FIG. 27.” of Tsou)
Regarding Claim 11, Tsou in view of Shin, in further view of MathWorks discloses the method of Claim 7.
Tsou discloses,
wherein determining an object comprises determining an object included in a range having the accumulated probability density less than or equal to the threshold probability density as a clutter.
(Col 16 Line 4-10; “Range profiles for a range bin should remain above the corresponding range bin in the threshold range profile signal for a majority of the duration of the Hough transform to be considered as authentic targets. Requiring range profiles to remain above the threshold range profile signal provides further discrimination of spurious data, clutter and noise signals which can trigger false alarms.” of Tsou)
Regarding Claim 12, Tsou in view of Shin, in further view of MathWorks discloses the method of Claim 7.
Tsou discloses,
further comprising providing information to a driver assistance system module for following a preceding vehicle included in the host vehicle. (Refer to Fig 14a, Element 100; and Col 11 Line 64-65; “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 12 Line 22-23 “The radar system 100 mounted on the vehicle 300, such as an automobile, can be used in intelligent cruise control systems, collision avoidance systems,” of Tsou)
(Col 12 Line 56-59 “The RSP 380 can be connected with an input and display device 384, a controller 386 and a memory 388 (which can include RAM, ROM etc.) via an interface 390,” of Tsou)
(Col 15 Line 35-37 “The controller 386 can determine whether a valid target exists and trigger the target decision device 500 into the tracking mode.” of Tsou)
Tsou does not disclose the limitation below, but Shin teaches,
Further comprising providing information about the stationary target
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process as taught by Tsou to include detection of stationary targets as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Regarding Claim 13,
Tsou discloses a radar device comprising:
A radar device comprising (Refer to Fig.1, element 10 of Tsou):
an antenna unit including at least one transmission antenna and at least one receiving antenna; (Refer to Fig 1, element 14 of Tsou)
a transceiver configured to control to transmit a transmission signal through the antenna unit and receive a reception signal reflected from an object; (Refer to Fig 2a, Col 2 Line 41-43; “a radar transceiver for generating transmit signals and for receiving signals reflected by targets.” of Tsou)
a signal processor configured to process the transmission signal and the reception signal to acquire target information; and (Refer to Fig 15, element 380 0f Tsou)
A target detection device (Refer to Fig.15, element 500 of Tsou)
A target detection device configured to determine... from a host vehicle,
(Refer to Fig 14a, Element 100; and Col 11 Line 64-65; “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
Tsou does not disclose:
A target detection device configured to determine a candidate area within a specific distance range
A target detection device configured to... determine a stationary target
Shin teaches the concept of:
A target detection device configured to determine a candidate area within a specific distance range
(Col 7 Line 7-9; “The feature point extraction module 132 may correct the movement amount of the vehicle when accumulating the detection data.” of Shin)
(Col 7 Line 12-15; “the feature point extraction module 132 may receive driving information of the vehicle from the driving information sensor and obtain the movement amount of the vehicle.” of Shin)
(Col 9 Line 35-39; “The false target determination module 134 may detects a pair of targets located on the left and right sides of the guardrail among the objects, in which a difference in minimum distance to the guardrail of the pair of targets is within a predetermined range,” of Shin)
Shin teaches:
A target detection device configured to... determine a stationary target
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the target detection device as taught by Tsou to include detection of stationary targets as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Tsou and Shin do not teach:
A target detection device configured to... create a kernel function for each of a plurality of range data included in the candidate area, determine an accumulated probability density by accumulating a plurality of kernel functions, and determines a target based on the accumulated probability density.
MathWorks teaches:
A target detection device configured to... create a kernel function for each of a plurality of range data included in the candidate area, determine an accumulated probability density by accumulating a plurality of kernel functions, and determines a target based on the accumulated probability density.
(Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
and (Fourth Paragraph of Kernel Smoothing Function “Alternatively, the kernel distribution builds the pdf by creating an individual probability density curve for each data value, then summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set.” of MathWorks)
Tsou, Shin and MathWorks are analogous as all disclose a data process and analysis workflow to identify and track radar reception signals that determine the distance of a target object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process of determining a target as taught by Tsou to include the kernel probability density function as taught by MathWorks. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou in order to accurately describe the distribution of data. (Overview of MathWorks)
Regarding Claim 14, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 13.
Tsou discloses
wherein the target detection device comprises: (Refer to Fig 15, element 500 of Tsou)
a histogram processor (Col 18, lines 63-67 “microprocessor” of Tsou) configured to create and update a histogram representing an object detection frequency for each range using the reception signal in response to a movement of a host vehicle;
(Refer to Fig 14a, Element 100; Col 11 Line 64-65 “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 16 Line 28-31; “The 2-D range-speed histogram is composed of a plurality of quantized bins which accumulate or count occurrences of samples having a value within each quantized bin. The count thus accumulated is hereinafter referred to as a "vote" for a quantized bin.” of Tsou)
a target determiner configured to determine a target range having a value equal to or greater than a threshold value , and determine an object included in the target range (Col 15 Line 21-25; “The target decision device 500, connected to the 2-D parameter estimation device 504, evaluates the 2-D estimation signal and determines whether a valid target exists. Only targets greater than the threshold range profile signal are analyzed using the Hough transform,” of Tsou)
(Col 15 Line 59-62; “As the range and speed data generated by the Hough transform is collected in the bins of the 2-D range-speed histogram, peaks are formed in the range-speed bins which identify a target range and speed at 510 in FIG. 27.” of Tsou)
Tsou does not disclose:
a candidate area determiner configured to determine the candidate area based on the detection data;
a target determiner configured to.... determine an object included in the range as the stationary target.
Shin teaches the concept of:
a candidate area determiner configured to determine the candidate area based on the detection data;
(Col 7 Line 16-18; “The feature point extraction module 132 may generate a predetermined region in the detection region of the radar as a region of interest (ROI).” of Shin)
(Col 7 Line 50-57; “detection data of the stationary object is present in the left region of interest 31 moved to the left, the feature point extraction module 132 may extract the feature points 40 from the left region of interest 31. Similarly, since the detection data of the stationary object exists in the right region of interest 51 moved to the right, the feature point extraction module 132 may extract the feature points 60 from the right region of interest 51.” of Shin)
(Col 7 Line 58-67 “the feature point extraction module 132 may be configured to extract feature points in the case that the number of detection data for the object existing in the region of interest is greater than or equal to a predetermined number. The predetermined number as the reference for the feature point extraction may be adjusted according to the size of the region of interest. According to an example, a point having the highest density of detection data in the region of interest may be extracted as the feature point.” of Shin)
Shin teaches:
a target determiner configured to.... determine an object included in the range as the stationary target.
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process of determining a target range as taught by Tsou to include detection of stationary targets as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Tsou and Shin does not teach:
a candidate area determiner configured to determine... based on the histogram;
a kernel density estimator configured to create the kernel function for each of the plurality of range data included in the candidate area and determine the accumulated probability density by accumulating a plurality of kernel functions; and
a target determiner configured to determine a target having an accumulated probability density equal to or greater than a threshold probability density
However, MathWorks discloses,
a candidate area determiner configured to determine... based on the histogram;
(Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
a kernel density estimator configured to create the kernel function for each of the plurality of range data included in the candidate area and determine the accumulated probability density by accumulating a plurality of kernel functions; and
(Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks) and
(Fourth Paragraph of Kernel Smoothing Function “Alternatively, the kernel distribution builds the pdf by creating an individual probability density curve for each data value, then summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set.” of MathWorks)
a target determiner configured to determine a target having an accumulated probability density equal to or greater than a threshold probability density, (Fourth Paragraph of Kernel Smoothing Function “Alternatively, the kernel distribution builds the pdf by creating an individual probability density curve for each data value, then summing the smooth curves. This approach creates one smooth, continuous probability density function for the data set.” of MathWorks)
Tsou, Shin and MathWorks are analogous as all disclose a data process and analysis workflow to identify and track radar reception signals that determine the distance of a target object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process of determining a target as taught by Tsou to include the kernel probability density function as taught by MathWorks. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou in order to accurately describe the distribution of data. (Overview of MathWorks)
Regarding Claim 15, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 14.
Tsou teaches,
wherein the histogram processor (Col 18, lines 63-67 “microprocessor” of Tsou) updates the histogram by accumulating or clustering object detection frequencies for objects within a driving path of the host vehicle at a plurality of time points.
(Refer to Fig 14a, Element 100; Col 11 Line 64-65 “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 16 Line 28-31; “The 2-D range-speed histogram is composed of a plurality of quantized bins which accumulate or count occurrences of samples having a value within each quantized bin. The count thus accumulated is hereinafter referred to as a "vote" for a quantized bin.” of Tsou)
Tsou does not disclose the limitation below, but Shin teaches
wherein the histogram processor updates....object detection frequencies for stationary objects
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify device as taught by Tsou to include detection of stationary targets as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Regarding Claim 16, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 15.
Tsou does not disclose the limitation below, but Shin teaches,
wherein the candidate area determiner determines an area having an object detection frequency greater than or equal to a threshold frequency as the candidate area.
(Col 6 Line 30-33; “The stationary object determination module 131 may determine the stationary object that is a target object for extracting the feature point among objects detected by the radar.” of Shin)
(Col 7 Line 16-18; “The feature point extraction module 132 may generate a predetermined region in the detection region of the radar as a region of interest (ROI).” of Shin)
(Col 7 Line 50-57; “detection data of the stationary object is present in the left region of interest 31 moved to the left, the feature point extraction module 132 may extract the feature points 40 from the left region of interest 31. Similarly, since the detection data of the stationary object exists in the right region of interest 51 moved to the right, the feature point extraction module 132 may extract the feature points 60 from the right region of interest 51.” of Shin)
(Col 7 Line 58-67 “the feature point extraction module 132 may be configured to extract feature points in the case that the number of detection data for the object existing in the region of interest is greater than or equal to a predetermined number. The predetermined number as the reference for the feature point extraction may be adjusted according to the size of the region of interest. According to an example, a point having the highest density of detection data in the region of interest may be extracted as the feature point.” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the device as taught by Tsou to include a candidate area to determine a detection frequency threshold based off of detection data as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Tsou in view of Shin does not disclose the following limitation. However, MathWorks discloses,
wherein the candidate area determiner determines an area including at least one range bin
(Second Paragraph of Kernel Smoothing Function “A histogram represents the probability distribution by establishing bins and placing each data value in the appropriate bin.” of MathWorks)
(Third Paragraph of Kernel Smoothing Function “Because of this bin count approach, the histogram produces a discrete probability density function.” of MathWorks)
Tsou, Shin and MathWorks are analogous as all disclose a data process and analysis workflow to identify and track radar reception signals that determine the distance of a target object.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the process of determining a target as disclosed by Tsou to include determining detection of histogram data as disclosed by MathWorks. One of ordinary skill in the art before the effective the effective filing date of the claimed invention would have been motivated to modify Tsou to accurately describe the data (Overview of MathWorks).
Regarding Claim 17, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 14.
Tsou discloses,
wherein the target determiner determines a range with the maximum accumulated probability density among one or more ranges with the accumulated probability density equal to or greater than the threshold value as the target range.
(Col 15 Line 21-25; “The target decision device 500, connected to the 2-D parameter estimation device 504, evaluates the 2-D estimation signal and determines whether a valid target exists. Only targets greater than the threshold range profile signal are analyzed using the Hough transform,” of Tsou) and
(Col 15 Line 59-62; “As the range and speed data generated by the Hough transform is collected in the bins of the 2-D range-speed histogram, peaks are formed in the range-speed bins which identify a target range and speed at 510 in FIG. 27.” of Tsou)
Regarding Claim 18, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 14.
Tsou discloses,
wherein the target determiner determines an object included in a range having the accumulated probability density less than or equal to the threshold probability density as a clutter.
(Col 16 Line 4-10; “Range profiles for a range bin should remain above the corresponding range bin in the threshold range profile signal for a majority of the duration of the Hough transform to be considered as authentic targets. Requiring range profiles to remain above the threshold range profile signal provides further discrimination of spurious data, clutter and noise signals which can trigger false alarms.” of Tsou)
Regarding Claim 19, Tsou in view of Shin, in further view of MathWorks discloses the device of Claim 14.
Tsou discloses,
wherein the target determiner provides information to a driver assistance system module for following a preceding vehicle included in the host vehicle. (Refer to Fig 14a, Element 100; and Col 11 Line 64-65; “a vehicle 300 incorporating the compact packaging radar system 100 is illustrated.” of Tsou)
(Col 12 Line 22-23 “The radar system 100 mounted on the vehicle 300, such as an automobile, can be used in intelligent cruise control systems, collision avoidance systems,” of Tsou)
(Col 12 Line 56-59 “The RSP 380 can be connected with an input and display device 384, a controller 386 and a memory 388 (which can include RAM, ROM etc.) via an interface 390,” of Tsou)
(Col 15 Line 35-37 “The controller 386 can determine whether a valid target exists and trigger the target decision device 500 into the tracking mode.” of Tsou)
Tsou does not disclose the following limitations. However, Shin discloses,
wherein the target determiner provides information about the stationary target
(Refer to Fig 1 and Col 3 Line 47-54; Referring to FIG. 1, the vehicle control apparatus 100 according to the present disclosure may include the radar 110 for receiving radar signals transmitted from outside the vehicle and reflected from objects around the vehicle and processing the received radar signals to obtain detection data for the objects, and the controller 130 for determining a stationary object among the objects based on the detection data” of Shin)
Tsou and Shin are analogous as both disclose automotive radar systems for signal processing and data analysis to identify, track, and make decisions about external targets.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention, to modify the device of determining a target as taught by Tsou to include detection of stationary targets as taught by Shin. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to modify Tsou to prevent tracking fake or false targets. (Col 1 Line 36-45 of Shin)
Conclusion
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/V.I.O./
Examiner, Art Unit 3648
/RESHA DESAI/Supervisory Patent Examiner, Art Unit 3648
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