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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/15/2025 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.
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.
Claims 1, 2, 6, 7, and 14-17 are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0005205 to Aoyama et al, hereinafter “Aoyama” in view of CN110378934A, hereinafter “CN’934.” Note that all citations to CN’934 referenced in the below rejection refer to the supplied English translation of CN’934.
With respect to claim 1, Aoyama teaches an image processing apparatus, comprising: one or more processors that execute a program stored in a memory and thereby function as: an acquisition unit configured to acquire, from an input image based on image data capturing a scene in which a plurality of subjects are moving in a substantially same direction, information regarding a positional relationship of the plurality of subjects in the direction (Aoyama, Fig 4, and [0043]-[0045] and [0049]), with use of a trained machine-learning model; and a determination unit configured to decide a main subject region from among subject regions respectively corresponding to the plurality of subjects in the image data (Aoyama, “subject tracking control unit 153” [0053]), based on the information acquired by the acquisition unit, wherein the determination unit determines whether or not to decide the main subject region, based on a reliability of the information, and decides the main subject region if it is determined to decide the main subject. (emphasis added)
Note that Aoyama teaches at [0047] that if it is determined that no main subject exists, or it is not possible to find a main subject, the process ends.
Thus, Aoyama fails to teach the bolded claim language above, specifically the use of machine learning for tracking the objects (people) and deciding a main subject dependent upon the reliability of the detected object information.
On the other-hand, CN’934 discloses in the same field of endeavor (camera object tracking of groups of objects and deciding the main subject) wherein at page 4, first paragraph the use machine learning is employed (CNN or RNN). CN’934 discloses at page 10, middle of the page that determination of the candidate subject for the main object can be determined via a confidence map, i.e. those objects that exceed the confidence threshold may be considered as a possible main subject. The confidence threshold can be set to any appropriate value. In addition, similar to the explicit teaching in Aoyama at [0047] of stopping the process if no main subject is detected, it would be inherent in the teachings of CN’934 that if no subject in the image has a confidence score above the predefined confidence threshold, then no subject would be chosen as the main subject.
Therefore, it would have been obvious before the effective filing date of the claimed invention to have provided the main subject tracking process of Aoyama with both a machine learning ability and the ability to decide whether or not to select a main subject depending on the confidence scores of the objects detected as taught by CN’934. Tracjing of potential main subjects is predicated upon accurate object recognition. To this end, machine learning has become ubiquitous in the image recognition art as evidence by CN’934. One of ordinary skill in the art would have recognized the benefits of machine learning, namely the very accurate ability of machine learning to identify different objects in a scene. Secondly, by assigning confidence thresholds to the detected objects, one of ordinary skill in the art would have expected to gain both power savings and processor efficiencies due to the fact that main object tracking would be halted in situations where no objects can be detected that exceed the confidence threshold.
With regard to claim 2, wherein the information is a positional order of the plurality of subjects in the direction, and the determination unit decides, as the main subject region, a subject region corresponding to a subject at a specific positional order among the plurality of subjects (Aoyama at [0053] and [0055]).
With regard to claim 6, wherein the specific positional order is first (Aoyama at [0053]).
With regard to claim 7, wherein the information is a likelihood of a subject being located at a front in the direction, and the determination unit decides, as the main subject region, a subject region corresponding to a subject of which the likelihood is highest among the plurality of subjects (Aoyama at [0053]-[0065], and in particular [0065]).
With regard to claim 14, wherein a magnitude of the difference in the likelihood between two subjects represents a magnitude of a distance between the two subjects in real space (CN’934 at top 1/3rd of page 11, “score values of different positions of the subject in the image”).
With regard to claim 15, Aoyama discloses an image capture apparatus (Aoyama, lens and body part 100/120 [0033]) comprising: the image processing apparatus according to claim 1; and autofocus detecting means for performing automatic focus detection that causes the main subject region decided by the image processing apparatus to be focused (Aoyama, “auto-focus control unit 152 [0035]).
Claims 16 and 17 are rejected based upon reasoning applied, mutatis mutandis, to that of claim 1 above. Furthermore, the non-transitory computer-readable medium and processor of claim 17 is disclosed by Aoyama at [0035].
Allowable Subject Matter
Claims 3-5 and 8-13 are 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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DAVID OMETZ
Primary Examiner
Art Unit 2672
/DAVID OMETZ/Primary Examiner, Art Unit 2672
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