DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant’s amendments to the claims, filed 6/22/2026, are accepted and appreciated by the examiner.
Response to Arguments
Applicant’s arguments filed 6/22/2022 have been fully considered. With regards to the 35 U.S.C. §101 Rejection Applicant’s amendments recite a practical application, and therefore the rejection has been withdrawn. With regards to the 35 U.S.C. §112(b) Applicant’s amendments have addressed these issues and are therefore withdrawn. Examiner notes that the amendments change the scope and clarity of the claims and a prior art rejection is presented below necessitated by the amendments.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 17 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claim 17 recites “the LiDAR (Light Detection and Ranging) system. There is insufficient antecedent basis for this limitation in the claim.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 12-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moody (US 2018/0172828 A1) in view of Xu (CN110210389 A).
With respect to Claim 1 Moody teaches A method for identifying a falling object based on a LiDAR (Light Detection and Ranging) system, the method for identifying a falling object comprising (See Abstract In an aspect, a processing apparatus receives detection signaling indicating a field incursion in a detection field of a LIDAR device mounted to a marine vessel and oriented to detect objects within a free space alongside the marine vessel): obtaining a point cloud data set provided by the LiDAR system, wherein the point cloud data set comprises point cloud data of the LiDAR system at a first moment and point cloud data of the LiDAR system at a second moment, and wherein the second moment follows the first moment (See Para[0061] The signal 132 may be a simple notification of a detection field incursion, along with other information such as size, location, etc. The signal 132 may also be a complex notification of detection field incursion, containing information typically associated with a point cloud detection.); identifying a dynamic point cloud cluster set to obtain an identified dynamic point could cluster set, based on the point cloud data at the first moment and the point cloud data at the second moment, wherein the dynamic point cloud cluster set comprises at least one dynamic point cloud cluster (See Para[0108] the point cloud data is converted to the normalized format by processing each point as it becomes available, to determine if an object has been detected. ); and determining whether the data representing the position of the dynamic point cloud cluster in the direction of gravity within a set time period meets a law of free fall (See Fig 11); determining that an object represented by the dynamic point cloud cluster is a falling object in response to a determination, that the data meets the law of free fall (See Para[0086]), and in response to a determination that the object represented by the dynamic point cloud cluster is a falling object, providing waring information to a vehicle to avoid approaching the falling object (See Para[0125] The method further comprises outputting fall-event signaling responsive to the fall event, to initiate one or more alarms or control actions taken in response to fall events.). However Moody is silent to the language of in response to the identified dynamic point cloud cluster set, performing tracking and determination process, wherein the tracking and determination process comprises: for each dynamic point cloud cluster in the dynamic point cloud cluster set, obtaining, based on point cloud data of the LiDAR system at subsequent moments that follow the second moment, a data set of a dynamic point cloud cluster, wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster in a direction of gravity Nevertheless Xu teaches in response to the identified dynamic point cloud cluster set, performing tracking and determination process, wherein the tracking and determination process comprises: for each dynamic point cloud cluster in the dynamic point cloud cluster set, obtaining, based on point cloud data of the LiDAR system at subsequent moments that follow the second moment, a data set of a dynamic point cloud cluster, wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster in a direction of gravity (See Para[0005],[0007] and Para[0059] First, obstacle targets are extracted using a clustering method based on Euclidean distance and density. Second, features are extracted from the segmented point cloud. Then, the extracted features are used to train a classifier for classification. Finally, the centroid is calculated as the center of the obstacle, and the obstacle is tracked using a state transition method. When obstacle point clouds are partially missing, the iterative nearest neighbor algorithm is used to directly process the clustered point clouds between two frames, which avoids errors caused by replacing the target centroid with the cluster centroid and improves tracking accuracy. Update the optimal state estimate of each target at the current moment, and predict the state at the next moment; the system monitors the position of dynamic obstacles in real time, and once it is predicted that the distance between the obstacle and the radar center is less than the preset threshold, an early warning mechanism is immediately issued to effectively reduce and avoid traffic accidents and secondary accidents during rescue operations.) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster such as that of Xu One of ordinary skill would have been motivated to modify Moody because using data set of the dynamic point cloud cluster comprising at least data representing a position of the dynamic point cloud cluster would improve tracking and accuracy.
With respect to Claim 2 Moody teaches The method for identifying a falling object according to claim 1, wherein the set time period is at least part of a time period between the second moment and the last moment at which the LiDAR system is capable of detecting the dynamic point cloud cluster. (Para[0097] and Fig. 11) With respect to Claim 12 Moody teaches The method for identifying a falling object according to claim 1, further comprising: determining whether a position of the dynamic point cloud cluster in the direction of gravity at a moment at which the dynamic point cloud cluster is identified meets the following condition:
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wherein l.sub.x(t.sub.0) represents the position of the dynamic point cloud cluster in the direction of gravity at the moment at which the dynamic point cloud cluster is identified, H.sub.G represents a ground height, and h is a threshold whereby it is determined whether the height meets a condition. (See Para[0101]-[0104])
With respect to Claim 13 Moody teaches The method for identifying a falling object according to claim 1, wherein the warning information comprises at least one of: an alarm indicating detection of the falling object and point cloud data representing at least one of a time at which the falling object is identified, and size, position and motion features of the falling object (See Para[0125]). With respect to Claim 14 Moody teaches The method for identifying a falling object according to claim 1, further comprising: triggering an image acquisition apparatus to photograph a video of the falling object, and/or storing the point cloud data in response to determining that the object represented by the dynamic point cloud cluster is the falling object (See Para[0127]). With respect to Claim 15 Moody teaches An apparatus for identifying a falling object based on a (Light Detection and Ranging) LiDAR system, the apparatus comprising(See Abstract In an aspect, a processing apparatus receives detection signaling indicating a field incursion in a detection field of a LIDAR device mounted to a marine vessel and oriented to detect objects within a free space alongside the marine vessel): at least one processor; and (See Para[0027]) at least one memory having a computer program comprising instructions stored thereon, (See Para[0027]) wherein the computer program, when executed by the at least one processor, causes the at least one processor to: obtain a point cloud data set provided by the LiDAR system, wherein the point cloud data set comprises point cloud data of the LiDAR system at a first moment and point cloud data of the LiDAR system at a second moment, and wherein the second moment follows the first moment(See Para[0061] The signal 132 may be a simple notification of a detection field incursion, along with other information such as size, location, etc. The signal 132 may also be a complex notification of detection field incursion, containing information typically associated with a point cloud detection.); identify a dynamic point cloud cluster set to obtain an identified dynamic point cloud cluster set, based on the point cloud data at the first moment and the point cloud data at the second moment, wherein the dynamic point cloud cluster set comprises at least one dynamic point cloud cluster; (See Para[0108] the point cloud data is converted to the normalized format by processing each point as it becomes available, to determine if an object has been detected. )and determine whether the data representing the position of the dynamic point cloud cluster in the direction of gravity within a set time period meets a law of free fall; (See Fig 11); determine that an object represented by the dynamic point cloud cluster is a falling object in response to a determination, that the data meets the law of free fall, (See Para[0086]) and in response to a determination that the object represented by the dynamic point cloud cluster is a falling object, provided warning information to a vehicle to avoid approaching the falling object. (See Para[0125] The method further comprises outputting fall-event signaling responsive to the fall event, to initiate one or more alarms or control actions taken in response to fall events.). However Moody is silent to the language of in response to the identified dynamic point cloud cluster set, performing tracking and determination process, wherein the tracking and determination process comprises: for each dynamic point cloud cluster in the dynamic point cloud cluster set, obtaining, based on point cloud data of the LiDAR system at subsequent moments that follow the second moment, a data set of a dynamic point cloud cluster, wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster in a direction of gravity Nevertheless Xu teaches in response to the identified dynamic point cloud cluster set, performing tracking and determination process, wherein the tracking and determination process comprises: for each dynamic point cloud cluster in the dynamic point cloud cluster set, obtaining, based on point cloud data of the LiDAR system at subsequent moments that follow the second moment, a data set of a dynamic point cloud cluster, wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster in a direction of gravity (See Para[0005],[0007] and Para[0059] First, obstacle targets are extracted using a clustering method based on Euclidean distance and density. Second, features are extracted from the segmented point cloud. Then, the extracted features are used to train a classifier for classification. Finally, the centroid is calculated as the center of the obstacle, and the obstacle is tracked using a state transition method. When obstacle point clouds are partially missing, the iterative nearest neighbor algorithm is used to directly process the clustered point clouds between two frames, which avoids errors caused by replacing the target centroid with the cluster centroid and improves tracking accuracy. Update the optimal state estimate of each target at the current moment, and predict the state at the next moment; the system monitors the position of dynamic obstacles in real time, and once it is predicted that the distance between the obstacle and the radar center is less than the preset threshold, an early warning mechanism is immediately issued to effectively reduce and avoid traffic accidents and secondary accidents during rescue operations.) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster such as that of Xu One of ordinary skill would have been motivated to modify Moody because using data set of the dynamic point cloud cluster comprising at least data representing a position of the dynamic point cloud cluster would improve tracking and accuracy.
With respect to Claim 16 Moody teaches The apparatus for identifying a falling object according to claim 15, further comprising the LiDAR system. (See Abstract) With respect to Claim 17 Moody teaches A non-transitory computer-readable storage medium having a computer program comprising instructions stored thereon, wherein the computer program, when executed by a processor, causes the processor to(See Para[0027]): obtain a point cloud data set provided by the LiDAR (Light Detection and Ranging)system, wherein the point cloud data set comprises point cloud data of the LiDAR system at a first moment and point cloud data of the LiDAR system at a second moment, and wherein the second moment follows the first moment(See Para[0061] The signal 132 may be a simple notification of a detection field incursion, along with other information such as size, location, etc. The signal 132 may also be a complex notification of detection field incursion, containing information typically associated with a point cloud detection.); identify a dynamic point cloud cluster set to obtain an identified dynamic point cloud cluster set based on the point cloud data at the first moment and the point cloud data at the second moment, wherein the dynamic point cloud cluster set comprises at least one dynamic point cloud cluster(See Para[0108] the point cloud data is converted to the normalized format by processing each point as it becomes available, to determine if an object has been detected. ); and in response to the identified dynamic point cloud cluster set, performing tracking and determination process, wherein the tracking and determination process comprises: for each dynamic point cloud cluster in the dynamic point cloud cluster set, obtaining, based on point cloud data of the LiDAR system at subsequent moments that follow the second moment, a data set of a dynamic point cloud cluster of the LiDAR system, wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster in a direction of gravity; determine whether the data representing the position of the dynamic point cloud cluster in the direction of gravity within a set time period meets a law of free fall(See Fig 11); determine that an object represented by the dynamic point cloud cluster is a falling object in response to a determination that the data meets the law of free fall(See Para[0086]), and in response to a determination that the object represented by the dynamic point cloud cluster is a falling object, providing warning information to a vehicle to avoid approaching the falling object. (See Para[0125] The method further comprises outputting fall-event signaling responsive to the fall event, to initiate one or more alarms or control actions taken in response to fall events.). However Moody is silent to the language of in response to the identified dynamic point cloud cluster set, performing tracking and determination process, wherein the tracking and determination process comprises: for each dynamic point cloud cluster in the dynamic point cloud cluster set, obtaining, based on point cloud data of the LiDAR system at subsequent moments that follow the second moment, a data set of a dynamic point cloud cluster, wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster in a direction of gravity Nevertheless Xu teaches in response to the identified dynamic point cloud cluster set, performing tracking and determination process, wherein the tracking and determination process comprises: for each dynamic point cloud cluster in the dynamic point cloud cluster set, obtaining, based on point cloud data of the LiDAR system at subsequent moments that follow the second moment, a data set of a dynamic point cloud cluster, wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster in a direction of gravity (See Para[0005],[0007] and Para[0059] First, obstacle targets are extracted using a clustering method based on Euclidean distance and density. Second, features are extracted from the segmented point cloud. Then, the extracted features are used to train a classifier for classification. Finally, the centroid is calculated as the center of the obstacle, and the obstacle is tracked using a state transition method. When obstacle point clouds are partially missing, the iterative nearest neighbor algorithm is used to directly process the clustered point clouds between two frames, which avoids errors caused by replacing the target centroid with the cluster centroid and improves tracking accuracy. Update the optimal state estimate of each target at the current moment, and predict the state at the next moment; the system monitors the position of dynamic obstacles in real time, and once it is predicted that the distance between the obstacle and the radar center is less than the preset threshold, an early warning mechanism is immediately issued to effectively reduce and avoid traffic accidents and secondary accidents during rescue operations.) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody wherein the data set of the dynamic point cloud cluster comprises at least data representing a position of the dynamic point cloud cluster such as that of Xu One of ordinary skill would have been motivated to modify Moody because using data set of the dynamic point cloud cluster comprising at least data representing a position of the dynamic point cloud cluster would improve tracking and accuracy.
Claim(s) 3, 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moody (US 2018/0172828 A1) in view of Xu (CN110210389 A) as applied to claim 1 above, and further in view of Shah (US 2024/0011775 A1). With respect to Claim 3 Moody teaches The method for identifying a falling object according to claim 1, wherein the obtaining the point cloud data set provided by the LiDAR system comprises: obtaining original point cloud data provided by the LiDAR system at different moments (See Par[0108]); and However Moody is silent to the language of calibrating, based on a mounting position and a mounting angle of the LiDAR system, the original point cloud data from a coordinate system of the LiDAR system until a coordinate axis of the coordinate system of the LiDAR system that is perpendicular to a laser emergent direction and points upward overlaps the direction of gravity, to obtain an adjusted point cloud data set. Nevertheless Shah teaches calibrating, based on a mounting position and a mounting angle of the LiDAR system, the original point cloud data from a coordinate system of the LiDAR system until a coordinate axis of the coordinate system of the LiDAR system that is perpendicular to a laser emergent direction and points upward overlaps the direction of gravity, to obtain an adjusted point cloud data set (See Para[0027],[0047]). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody and calibrate such as that of Shah. One of ordinary skill would have been motivated to modify Moody, because calibrating would improve accuracy. With respect to Claim 4 Moody teaches The method for identifying a falling object according to claim 3, wherein the obtaining the point cloud data set provided by the LiDAR system further comprises: performing preprocessing on the adjusted point cloud data set, the preprocessing comprising at least one of the following: noise cleaning and dynamic point extraction (See Para[0051]).
Claim(s) 5, 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moody (US 2018/0172828 A1) in view of Xu (CN110210389 A) as applied to claim 1 above, and further in view of Zhu (US 11,048,277 B1).
With respect to Claim 5 Moody teaches The method for identifying a falling object according to claim 1, wherein the determining whether the data representing the position of the dynamic point cloud cluster in the direction of gravity within a set time period meets a law of free fall comprises: is less than or equal to a threshold. (See Para[0087]) However Moody is silent to the language of determining whether a difference between a position of the dynamic point cloud cluster in the direction of gravity and an expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall Nevertheless Zhu teaches determining whether a difference between a position of the dynamic point cloud cluster in the direction of gravity and an expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall (See Col 35 lines 1-13) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody and determining whether the data meets the law of free fall such as that of Zhu. One of ordinary skill would have been motivated to modify Moody, because the problem can be simplified for objects that are in motion according to a known and predictable motion model. For example, an object in free fall is expected to continue along a previous trajectory while accelerating at rate based on a known gravitational constant, and would be no more than predictable use of prior art elements according to their established functions. With respect to Claim 6 Moody is silent to the language of The method for identifying a falling object according to claim 5, wherein the difference is a deviation, at each subsequent moment of the subsequent moments within the set time period, between the position of the dynamic point cloud cluster in the direction of gravity and the expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall. Nevertheless Zhu teaches wherein the difference is a deviation, at each subsequent moment of the subsequent moments within the set time period, between the position of the dynamic point cloud cluster in the direction of gravity and the expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall. (See Col 35 lines 1-13) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody and determining whether the data meets the law of free fall such as that of Zhu. One of ordinary skill would have been motivated to modify Moody, because the problem can be simplified for objects that are in motion according to a known and predictable motion model. For example, an object in free fall is expected to continue along a previous trajectory while accelerating at rate based on a known gravitational constant, and would be no more than predictable use of prior art elements according to their established functions.
Claim(s) 7-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moody (US 2018/0172828 A1) in view of Xu (CN110210389 A) and Zhu (US 11,048,277 B1) as applied to claim 5 above, and further in view of Larsson (US 2011/0066313A1). With respect to Claim 7 Moody is silent to the language of The method for identifying a falling object according to claim 5, wherein the difference is a mean square error of a deviation, within the set time period, between the position of the dynamic point cloud cluster in the direction of gravity and the expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall. Nevertheless Larsson teaches wherein the difference is a mean square error of a deviation, within the set time period, between the position of the dynamic point cloud cluster in the direction of gravity and the expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall. (See Para[0051],[0052]) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody wherein the difference is a mean square error of a deviation such as that of Larsson. One of ordinary skill would have been motivated to modify Moody, because an agreement between the expected distance and the corresponding distance actually measured from the laser range scanner can be determined to see how an object is moving to what is expected. With respect to Claim 8 Moody is silent to the language of The method for identifying a falling object according to claim 7, wherein the mean square error is calculated based on the following objective function:
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wherein a is a set constant; l.sub.{circumflex over (x)}(t.sub.i) represents an expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall when the point cloud data of the LiDAR system is obtained for the i.sup.th time; l.sub.x(t.sub.i) represents a position of the dynamic point cloud cluster in the direction of gravity when the point cloud data of the LiDAR system is obtained for the i.sup.th time; and i is any natural number between 0 and n, n representing a total number of times the point cloud data of the LiDAR system is obtained within the set time period. Nevertheless Larsson teaches wherein the mean square error is calculated based on the following objective function:
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wherein a is a set constant; l.sub.{circumflex over (x)}(t.sub.i) represents an expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall when the point cloud data of the LiDAR system is obtained for the i.sup.th time; l.sub.x(t.sub.i) represents a position of the dynamic point cloud cluster in the direction of gravity when the point cloud data of the LiDAR system is obtained for the i.sup.th time; and i is any natural number between 0 and n, n representing a total number of times the point cloud data of the LiDAR system is obtained within the set time period. (See Para[0051],[0052]) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody and calculate such as that of Larsson. One of ordinary skill would have been motivated to modify Moody, because such a calculation would determine an agreement between the expected distance and the corresponding distance actually measured from the laser range scanner and see how an object is moving to what is expected. With respect to Claim 9 Moody is silent to the language of The method for identifying a falling object according to claim 8, wherein the set constant is based on at least one of the following factors: an air resistance coefficient, an air density, a windward area of the object represented by the dynamic point cloud cluster, and a velocity of the object relative to air. Nevertheless Zhu teaches wherein the set constant is based on at least one of the following factors: an air resistance coefficient, an air density, a windward area of the object represented by the dynamic point cloud cluster, and a velocity of the object relative to air (See Col 35 lines 1-5). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody and have a constant such as that of Zhu. One of ordinary skill would have been motivated to modify Moody, because other known factors could improve the accuracy of the model.
Claim(s) 10-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Moody (US 2018/0172828 A1) in view of Xu (CN110210389 A) and Zhu (US 11,048,277 B1) as applied to claim 6 above, and further in view of Openstax (University Physics Vol1). With respect to Claim 10 Moody is silent to the language of The method for identifying a falling object according to claim 6, wherein the expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall is determined according to the following formula:
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wherein l.sub.{circumflex over (x)}(t.sub.i) represents an expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall at a current moment; l.sub.x(t.sub.0) represents a position of the dynamic point cloud cluster in the direction of gravity at a moment at which the dynamic point cloud cluster is identified; v.sub.x(t.sub.0) represents a velocity of the dynamic point cloud cluster in the direction of gravity at the moment at which the dynamic point cloud cluster is identified; t.sub.i represents a time difference between the subsequent moment and the moment at which the dynamic point cloud cluster is identified; and g is a gravitational acceleration. Nevertheless Openstax teaches wherein the expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall is determined according to the following formula:
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wherein l.sub.{circumflex over (x)}(t.sub.i) represents an expected position of the dynamic point cloud cluster in the direction of gravity that meets the law of free fall at a current moment; l.sub.x(t.sub.0) represents a position of the dynamic point cloud cluster in the direction of gravity at a moment at which the dynamic point cloud cluster is identified; v.sub.x(t.sub.0) represents a velocity of the dynamic point cloud cluster in the direction of gravity at the moment at which the dynamic point cloud cluster is identified; t.sub.i represents a time difference between the subsequent moment and the moment at which the dynamic point cloud cluster is identified; and g is a gravitational acceleration. (See Eq3.16) It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody use the equation such as that of Openstax. One of ordinary skill would have been motivated to modify Moody, because using such an equation would be no more than predictable use of prior art elements according to their established functions. With respect to Claim 11 Moody is silent to the language of The method for identifying a falling object according to claim 10, wherein the velocity v.sub.x(t.sub.0) of the dynamic point cloud cluster in the direction of gravity at the moment at which the dynamic point cloud cluster is identified is determined according to the following equation:
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wherein l.sub.x(t.sub.0) represents the position of the dynamic point cloud cluster in the direction of gravity at the moment at which the dynamic point cloud cluster is identified, l.sub.x(t.sub.1) represents a position of the dynamic point cloud cluster in the direction of gravity at a next moment of the moment at which the dynamic point cloud cluster is identified, and t represents a time interval between any two adjacent moments. Nevertheless Openstax teaches The method for identifying a falling object according to claim 10, wherein the velocity v.sub.x(t.sub.0) of the dynamic point cloud cluster in the direction of gravity at the moment at which the dynamic point cloud cluster is identified is determined according to the following equation:
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wherein l.sub.x(t.sub.0) represents the position of the dynamic point cloud cluster in the direction of gravity at the moment at which the dynamic point cloud cluster is identified, l.sub.x(t.sub.1) represents a position of the dynamic point cloud cluster in the direction of gravity at a next moment of the moment at which the dynamic point cloud cluster is identified, and t represents a time interval between any two adjacent moments (See Section Instantaneous Velocity and Eq 3.4 and 3.5). It would have been obvious to one of ordinary skill in the art before the effective filing date to modify Moody use the equation such as that of Openstax. One of ordinary skill would have been motivated to modify Moody, because using such an equation would be no more than predictable use of prior art elements according to their established functions.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action. Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSHIHISA ISHIZUKA whose telephone number is (571)270-7050. The examiner can normally be reached M-F 11:00-7:00.
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YOSHIHISA . ISHIZUKA
Examiner
Art Unit 2857
/YOSHIHISA ISHIZUKA/Primary Examiner, Art Unit 2857
