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) is in compliance 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.
Claims 1 – 6 and 12 – 23 are rejected under 35 U.S.C. 103 as being obvious over Valdes Garcia (US 20220026561 A1) in view of Valo (US 8063815 B2).
Note: Unless otherwise specified, all citations are that of the primary reference. Also, a strike-through indicates the feature is not taught.
As to claims 1, 12 and 18, Garcia discloses a detection apparatus comprising: at least one memory configured to store instructions; and at least one processor configured to execute the instructions to perform operations comprising:
determining a position of a subject in a 3D radar image (Fig. 2 Step 220 Para. 19 “each region of interest 152 could be a bounding box or a shape outline for a potential subject, or a region in a 2D or 3D space.”);
extracting a 3D (Para. 19 “3D space” Fig. 4A shows an active device relative a to subject and a reflecting object relative to an animal. See also Fig. 4B. Also, Para. 28 states “Each of the regions of interest may be defined by a border, a bounding box, or some other geometric shape that encompasses a potential subject.” – further suggesting that the active devices or reflecting objects are in relation to a subject; e.g., person or animal. Similar reasoning applies to Para. 32 “Determining (250) a concealed object classification may include using a machine learning classifier, or the like, that determines a concealment classification information for at least a portion of the region of interest.”);
(Para. 32 “Determining (250) a concealed object classification may include using a machine learning classifier, or the like, that determines a concealment classification information for at least a portion of the region of interest.” Figs. 4A – 4B suggest that the classification types to be a reflecting object or an active device.); and
detecting an object in the 3D sub-image by using the selected learned model (Fig. 2 step 250. See also Para. 32 “The process of determining the concealed object classification may be intentionally biased to reduce false negatives (i.e., incorrect selection of the ‘no detected object’ classification).”).
Garcia does not teach the selection of a learned model based on either one of a size of the 3D sub image or a type of the subject.
In the same field of endeavor, Valo teaches “the system includes means for selecting a set of training silhouettes of model objects with similar size parameters as in the observed object and which is adapted to retrieve said selected set from the database. The system also includes means for comparing the silhouette of the observed object with said set of selected training silhouettes, said means being adapted to classify said observed object as being of the class corresponding to the training silhouettes which best matches the silhouette of the observed object.” Here, the silhouette would meet the scope of a sub-image (col. 2 ll. 27 – 35).
In view of the teachings of Valo, it would have been obvious to apply training silhouettes to the active devices or reflecting objects as shown in Garcia’s Figs. 4A – 4B in order to more accurately determine the type of object and size thereby reducing the possibility of an incorrect selection thereby improving accuracy. The silhouettes would also save processing time by reducing the size of the image.
As to claims 2, 13 and 19, Garcia in view of Valo teaches the detection apparatus according to claim 1, 12 and 18 wherein the operations further comprising determining the one of extraction sizes to be used for extracting of the 3D sub-image based on subject information in which at least an extraction size is associated with each of a plurality of subject IDs and the subject ID of the subject included in the 3D radar image (As modified by Valo’s teachings of a silhouettes.).
As to claims 3, 14 and 20, Garcia in view of Valo teaches the detection apparatus according to claim 2, wherein the operations further comprising determining the subject ID to be used for determining the one of extraction sizes by identifying the type of the subject included in the 3D radar image (As modified by Valo’s teachings of silhouettes that includes comparable sizes.).
As to claim 4, 15 and 21, Garcia in view of Valo teaches the detection apparatus according to claim 1, 12 and 18 wherein the one of extraction sizes used for extracting of the 3D sub-image is a subject extraction size for extracting the whole of the subject (As modified by Valo’s teachings of silhouettes that includes comparable sizes that are corrected for range – the motivation being accuracy. See Valo claim 15)
As to claims 5, 16 and 22, Garcia in view of Valo teaches the detection apparatus according to claim 1 any one of claims 1 to 3, wherein the one of extraction sizes used for extracting of the 3D sub-image is a part extraction size for extracting only a part of the subject, and the reference position indicates a position of the part of the subject (Garcia Figs. 4A – 4B and as modified by Valo’s teachings of silhouettes that includes comparable sizes that are corrected for range – the motivation being accuracy. See Valo claim 15. The correction for range indicates the radar source as the reference.)
As to claims 6, 17 and 23, Garcia in view of Valo teaches the detection apparatus according to claim 1 any one of claims 1 to 5, wherein the operations further comprise the detection unit outputs outputting at least one of information indicating whether the object exists or not, a class of the detected object, and position information of the object (Garcia’s Figs. 4A – 4B).
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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