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 .
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.
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 and 4-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 2016-219004 A to Gaidon et al., hereinafter, “Gaidon” in view of JP 2004-046647 A to Asada et al., hereinafter, “Asada”.
Claim 1. Gaidon teaches an object tracking processing apparatus comprising: Gaidon [0019] Referring to FIG. 1, a computer-implemented system 10 for object tracking is illustrated.
at least one memory storing instructions, [0019] FIG. 1, memory 22
and at least one processor configured to execute the instructions to; [0019] FIG. 1, a processor device 26 in communication with the memory for executing the instructions.
and assign a tracking ID for identifying an object belonging to the similar object group to the object. [0029] The filtered set of objects thus identified is then saved in memory by location, category, and category ID (e.g., person 1). Examiner understands category to be the group with the category ID to be the tracking ID of the object.
[0053] …tracking information 20 may be output, such as for each frame, one or more of the IDs and categories of the detected targets, and their locations. FIG. 2 S134
Gaidon fails to explicitly teach calculate at least one similar object group including at least one object similar to a tracking target object. Asada, in the same field of object detection in image data teaches calculate at least one similar object group including at least one object similar to a tracking target object, [0018] color information (color parameter) of a uniform is acquired as a feature amount of a person, and team classification of players is performed based on the color information
Asada [0020] The feature amount is a parameter unique to each player or ball (each character) necessary for tracking each player or ball, that is, each individual character, and here, as the feature amount, position information and color information necessary for team classification (the above-described optimal color parameter P) are acquired.
on the basis of at least a feature amount of the tracking target object; Asada [0018] color information (color parameter) of a uniform is acquired as a feature amount of a person, and team classification of players is performed based on the color information
Asada [0020] The feature amount is a parameter unique to each player or ball (each character) necessary for tracking each player or ball, that is, each individual character, and here, as the feature amount, position information and color information necessary for team classification (the above-described optimal color parameter P) are acquired.
Asada also teaches processing apparatus [0020] FIG. 1 is a block diagram showing the overall arrangement of an embodiment of a tracking system to which a moving object
Asada also teaches the processor and memory [0010] Further, the present invention can be implemented in the form of a program of a processor such as a computer … can also be implemented in the form of a storage medium storing such a program.
Gaidon teaches tracking multiple objects in different categories (groups) in image data. Thus before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Gaidon with the teachings of Asada [Advantage] of team (group) classification based on feature amount to track each player/ball (objects) with high accuracy.
Claim 4. Gaidon further teaches further comprising an object tracking information storage unit configured to store the tracking ID assigned. Gaidon [0029] The filtered set of objects thus identified is then saved in memory by location, category, and category ID (e.g., person 1).
Gaidon [0051] …the ID of each identified (visible) target and its location in the frame are stored., FIG. 2 S130
Claim 5. Gaidon and Asada further teaches wherein the at least one processor is further configured to execute the instructions to detect the tracking target object in each frame configuring a video and the feature amount of the tracking target object; Asada [0018] color information (color parameter) of a uniform is acquired as a feature amount of a person, and team classification of players is performed based on the color information
[0034] each successive frame
and the object tracking processing apparatus further comprising an object feature amount storage unit configured to store, Asada [0031] Preferably, as shown in FIG. 8 b, the position information in all the frames is stored for each player or each tracked person, and the predicted position in the next frame is linearly predicted based on this storage.
for each object detected, a position of the object, a detection time of the object, a feature amount of the object, and a group ID assigned to the object, Asada [0020] The feature amount is a parameter unique to each player or ball (each character) necessary for tracking each player or ball, that is, each individual character, and here, as the feature amount, position information and color information necessary for team classification
Gaidon [0026] considering frame 14, at time t, a prediction is made for each candidate object 72,74,76,78 that is likely to occur at time t + 1 in frame 16.
Gaidon [0029] The filtered set of objects thus identified is then saved in memory by location, category, and category ID (e.g., person 1).
Gaidon [0051] …the ID of each identified (visible) target and its location in the frame are stored. FIG. 2 S130
wherein the at least one processor is further configured to execute the instructions to refer to a part or all of the object feature amount storage unit Asada [0015] extracting and tracking a player, a ball, or the like as described above is not limited to the form of computer software, but can be implemented in the form of a microprogram processed by a DSP (digital signal processor)
to calculate at least one similar object group including at least one object similar to the tracking target object, on the basis of at least the feature amount of the tracking target object. Asada [0018] color information (color parameter) of a uniform is acquired as a feature amount of a person, and team classification of players is performed based on the color information
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 2016-219004 A to Gaidon et al., hereinafter, “Gaidon” in view of JP 2004-046647 A to Asada et al., hereinafter, “Asada” and in further view of US 2022/0180639 A1 to Ono et al., hereinafter, “Ono”.
Claim 3. Gaidon fails to explicitly teach execute processing of assigning the tracking ID for identifying the object belonging to the similar object group to the object. Ono, in the same field of object detection in image data, teaches wherein the at least one processor is further configured to execute the instructions to parallelly execute processing of assigning the tracking ID for identifying the object belonging to the similar object group to the object. [0092] teaches assigning object IDs to groups and the object tracking performed in parallel.
Gaidon teaches tracking multiple objects in different categories (groups) in image data. Thus before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Gaidon with the teachings of Ono [0006-0009] to track objects in real-time by grouping objects.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over JP 2004-046647 A to Asada et al., hereinafter, “Asada” in view of JP 2016-219004 A to Gaidon et al., hereinafter, “Gaidon”.
Claim 6. Asada teaches an object grouping processing step of calculating at least one similar object group including at least one object similar to a tracking target object, on the basis of at least a feature amount of the tracking target object; [0018] color information (color parameter) of a uniform is acquired as a feature amount of a person, and team classification of players is performed based on the color information,
[0020] FIG. 2, the extractor 1b extracts a person and a ball, acquires a feature amount, and determines a team
Asada fails to explicitly teaches assigning a tracking ID for identifying an object belonging to the similar object group to the object. Gaidon, in the same field of object detection in image data teaches and an object tracking step of assigning a tracking ID for identifying an object belonging to the similar object group to the object. [0053] …tracking information 20 may be output, such as for each frame, one or more of the IDs and categories of the detected targets, and their locations. FIG. 2 S134
Asada teaches tracking objects in image data. Thus before the effective filing date of the present application, it would have been obvious to one of ordinary skill in the art to combine the teachings of Asada with the teachings of Gaidon [0015] to provide high accuracy, scalability, and transferability.
Allowable Subject Matter
Claim 7 is allowable.
The following is an examiner’s statement of reasons for allowance: the innovation that makes claim 7 allowable is “executing batch processing of assigning a tracking ID for identifying an object belonging to the similar object group to the object with reference to the object group information storage unit, at predetermined intervals”.
Claim 2 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. The innovation that makes claim 2 allowable is “the batch processing is processing of acquiring updated information relevant to the object belonging to the similar object group from the object group information storage unit and assigning the tracking ID for identifying the object belonging to the similar object group to the object, on the basis of the acquired information”.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DELOMIA L GILLIARD whose telephone number is (571)272-1681. The examiner can normally be reached 8am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, John Villecco can be reached at (571) 272-7319. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DELOMIA L GILLIARD/Primary Examiner, Art Unit 2661