DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Clams 1 – 13 are rejected under U.S.C. 103 as being obvious over Sugiura (US 20170345180 A1) in view of Kim (US 20220099838 A1).
As to claims 1 and 6, Sugiura discloses an object recognition device comprising at least one processor (Fig. 2) configured to implement:
a time measurement circuitry to measure a time (Fig. 10 S200);
a data receiving unit receiver to receive detection data of an object from each of a plurality of sensors and associate the time measured by the time measurement circuitry as an associated time with each of the received detection data (Figs. 6a-c);
a received data processing circuitry to calculate a detection data density in a specific region set using prediction data on a basis of the detection data (S204);
a prediction processing circuitry to predict a state value of the object corresponding to the associated time associated by the data receiving unit receiver from a state value of the object at an immediately preceding associated time and generate a prediction result as the prediction data (Para. 195 “processing circuit 14A may predict movement of the object 30F at the first timing t1′ from the moving direction and the moving speed of the same object 30F at the second timing t2′.”);
an adjusted determination region parameter generation unit generator to generate an adjusted determination region parameter by adjusting a parameter indicating a size of a determination region on a basis of the detection data density (Para. 87 “The shape fitting function 10M adjusts the variables of a shape model to define one shape model for which the evaluating function has a minimum value.”),
the determination region being used for determining whether the prediction data and the detection data are based on the same object (Para. 77 “the information processing device 10 determines whether each detection point 32 represents the same object 30 to which the previous detection points 32 represents.”);
Sugiura does not discloses a correlation processing circuitry to generate correlation data indicating a correlation between the prediction data and the detection data corresponding to the associated time in the adjusted determination region indicated by the adjusted determination region parameter; and an update processing circuitry to update the state value of the object on a basis of the correlation data.
In the same field, Kim teaches “in which “association” is performed, it may be determined whether there is a correlation between a predicted tracking box and a plurality of segment boxes of a target object that is being tracked, and information on the segment box having a correlation may be selected. For example, the presence or absence of the correlation is determined through comparison between the points of boxes. The correlation between the tracking box and the segment box may be determined using an Euclidean distance or a Mahalanobis distance. When the determined distance satisfies an allowable boundary value, it may be determined that there is a correlation (Para. 144).”
In view of Kim, it would have been obvious to the artisan before filing to apply clustering to more correctly determine which detection points belong to which track thereby better determining and/or updating which points belong to which tracks thereby improving accuracy.
As to claims 2 and 7, Sugiura in view of Kim teaches the object recognition device according to claims 1 and 6 wherein the adjusted determination region parameter generation unit generator sets the adjusted determination region parameter to a determination region parameter maximum value set in advance when the detection data density is less than a detection data density lower limit value, sets the adjusted determination region parameter to a determination region parameter minimum value set in advance when the detection data density is greater than a detection data density upper limit value, and decreases the adjusted determination region parameter in proportion to the detection data density when the detection data density is within a range from the detection data density lower limit value to the detection data density upper limit value (Sugiura Paras. 87 – 89 and Fig. 3).
As to claim 3, Sugiura in view of Kim teaches the object recognition device according to claim 1 wherein the received data processing circuitry determines whether the object is a stationary object or a moving object on a basis of ground velocity of the object calculated from the detection data, and calculates the detection data density separately in a case of the stationary object and in a case of the moving object (Sugiura Para. 27. The Examiner takes official notice that a stationary object can be used to determine own-vehicle speed, motion compensation and calibration. Also, tracking implies that stationary and mobile objects are processed separately.).
As to claim 4, Sugiura in view of Kim teaches the object recognition device according to any one of claim claim 1,wherein, when the detection data density is calculated, the received data processing circuitry determines the specific region on a basis of one or more of a position, velocity, and acceleration of the detection data (Sugiura Figs. 4a-d).
As to claim 5, Sugiura in view of Kim teaches the object recognition device according to any one of claims 1 wherein the received data processing circuitry resets a filtered value of the detection data density to the detection data density (Sugiura Fig. 5 S112 “NO” see also Para. 87).
As to claim 8, Sugiura in view of Kim teaches the object recognition device according to claim 2, wherein the received data processing circuitry determines whether the object is a stationary object or a moving object on a basis of ground velocity of the object calculated from the detection data, and calculates the detection data density separately in a case of the stationary object and in a case of the moving object (Sugiura Para. 27. The Examiner takes official notice that a stationary object can be used to determine own-vehicle speed, motion compensation and calibration. Also, tracking implies that stationary and mobile objects are processed separately.).
As to claim 9, Sugiura in view of Kim teaches the object recognition device according to claim 2, wherein, when the detection data density is calculated, the received data processing circuitry determines the specific region on a basis of one or more of a position, velocity, and acceleration of the detection data (Sugiura Figs. 4a-d).
As to claim 10, Sugiura in view of Kim teaches the object recognition device according to claim 2, wherein the received data processing circuitry resets a filtered value of the detection data density to the detection data density (Sugiura Fig. 5 S112 “NO” & Para. 87).
As to claim 11, Sugiura in view of Kim teaches the object recognition device according to claim 3, wherein, when the detection data density is calculated, the received data processing circuitry determines the specific region on a basis of one or more of a position, velocity, and acceleration of the detection data (Sugiura Figs. 4a-d).
As to claim 12, Sugiura in view of Kim teaches the object recognition device according to claim 3, wherein the received data processing circuitry resets a filtered value of the detection data density to the detection data density (Sugiura Fig. 5 S112 “NO” & Para. 87).
As to claim 13, Sugiura in view of Kim teaches the object recognition device according to claim 4, wherein the received data processing circuitry resets a filtered value of the detection data density to the detection data density (Sugiura Fig. 5 S112 “NO” & Para. 87).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W JUSTICE whose telephone number is (571)270-7029. The examiner can normally be reached 7:30 - 5:30 M-F.
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/MICHAEL W JUSTICE/Examiner, Art Unit 3648
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