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 .
Status of the Claims
Claims 1-11 were pending. Claims 1, 10, 11 have been amended. No claims have been canceled and no new claims have been added. Thus claims 1-11 are currently pending including independent claims 1, 10, 11.
Specification
The objection to the specification is removed in light of the remarks and amendments filed 05/15/2026.
Claim Rejections - 35 USC § 112
The rejections under 35 U.S.C. 112(b) are removed in light of the remarks and amendments filed 05/15/2026.
Claim Rejections - 35 USC § 103
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, 8, 10, 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoon (US 20220301188 A1) in view of Grill (US 20210383225 A1).
Regarding claim 1, Yoon discloses an estimation apparatus comprising: a memory that stores a first model and a second model that have been trained through machine learning for subject tracking (Fig. 1, [0047] apparatus including a memory; [0056]-[0057] multiple neural network models may be stored in the memory, the neural networks being trained to track/determine location of target objects within images); and
a processor that receives an imaging signal from an imaging element (Fig. 1, [0047] a processor; [0050] the processor receiving an image signal; Fig. 9, [0133], [0136] the processor receiving the input image which is generated by a camera), wherein the processor is configured to execute:
a decision process of deciding on a tracking subject of a tracking target (Fig. 2, [0072] a decision of an ROI object to be tracked is made);
a first creation process of creating a first reference image for the first model including the tracking subject (Fig. 2, [0074] the detection in the first frame is used to create a reference image which will be used in detecting in the second frame; [0080]-[0081] the reference image is used by the CNN for object tracking in the next frame);
a selection process of selecting one of the first model or the second model as a selected model based on factor information, wherein the factor information comprises at least one of information related to the tracking subject or information related to imaging settings of the imaging element (Fig. 3, [0094]-[0095] one of the available neural network models is chosen based on factor information (e.g. object feature values, aspect ratio values, network training) to perform object tracking on the present object; Fig. 4, [0101] in operation 420, the object tracking apparatus may select one neural network model from among a plurality of neural network models based on a feature value of the target object [0105] a feature of a target object may include a size, an aspect ratio, or a type of the target object, degree of movement etc.; (see also [0008], [0055]));
an input process of inputting a captured image represented by the imaging signal into the selected model (Fig. 3, [0092] an image is input into the selected neural network model); and
an estimation process of estimating a position of the tracking subject from within the captured image by using the selected model and a reference image for the selected model out of the first reference image and the second reference image (Fig. 4, [0099] using the reference created in a first frame, [0101] and using the selected neural network model, [0103] to generate an estimation of the location of the target object in the current image frame).
Yoon fails to disclose a second reference image for the second model including the tracking subject based on the imaging signal.
Grill, in a related system from the same field of endeavor of image processing using machine learning models for purposes including subject tracking (Abstract, [0002]), discloses a first creation process of creating a first reference image for the first model including the tracking subject and a second reference image for the second model including the tracking subject based on the imaging signal (Fig. 2, [0075] first and second transformations are applied to a data item to generate first and second views (i.e. reference images) of the data item, the first and second views (i.e. reference images) are each generated in order to be input to two different neural networks (e.g. target NN and online NN); [0046] wherein the data item is images; [0047] wherein the system is performing object tracking across images/frames).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine Grill with Yoon and create a first reference image for the first model including the tracking subject and a second reference image for the second model including the tracking subject based on the imaging signal, as disclosed by Grill, as part of an estimation apparatus for subject tracking, as disclosed by Yoon, for the purposes of achieving high accuracy and efficiency of the trained machine-learning system for object tracking (see Grill: [0099], [0031]-[0032]).
Regarding claim 8, Yoon in view of Grill discloses the estimation apparatus according to claim 1 as applied above. Yoon further discloses wherein the processor is configured to: execute a second update process of updating the first reference image and the second reference image based on a change in size of the tracking subject within an angle of view of the captured image (Fig. 2, [0084], [0087] updating the reference image based on a new frame including updated the size of the object).
Regarding claim 10, Yoon in view of Grill discloses everything claimed as applied above (see rejection of claim 1).
Regarding claim 11, Yoon in view of Grill discloses everything claimed as applied above (see rejection of claim 1). Yoon further discloses a non-transitory computer-readable storage medium storing a program ([0069] non-transitory computer-readable medium storing instructions executable by a processor).
Claim(s) 2, 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yoon (US 20220301188 A1) in view of Grill (US 20210383225 A1) in further view of Kim (US 20210073945 A1).
Regarding claim 2, Yoon in view of Grill discloses the estimation apparatus according to claim 1 as applied above.
Yoon fails to disclose wherein the second model has a larger number of layers or a larger layer size than that of the first model.
Kim, in a related system from the same field of image processing including selecting from among neural networks (Abstract), discloses wherein the second model has a larger number of layers or a larger layer size than that of the first model (Fig. 5, Fig. 7, [0211], [0213]-[0214] some of the neural network models which are available to be selected have a higher complexity than others; [0168] wherein complexity corresponds to the number of layers in the network or other indicators of complexity).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Yoon in view of Grill and include a second model which has a larger number of layers of a larger layer size than that of the first model, as disclosed by Kim, as part of an estimation apparatus for subject tracking, as disclosed by Yoon in view of Grill, for the purposes of extracting specific information from images and improving efficiency (See Kim: [0049], [0003]).
Regarding claim 9, Yoon in view of Grill discloses the estimation apparatus of claim 8 as applied above.
Yoon fails to disclose wherein the processor is configured to: execute the second update process based on a change in imaging magnification of an imaging apparatus including the imaging element.
Kim, in a related system from the same field of image processing including selecting from among neural networks (Abstract), discloses wherein the processor is configured to: execute the second update process based on a change in imaging magnification of an imaging apparatus including the imaging element (Fig. 10, [0232], [0235] a level of magnification of an image determined by a user of an apparatus is used to update the reference image and network selection).
It would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to combine Kim with Yoon in view of Grill and execute a second update based on a change in imaging magnification, as disclosed by Kim, as part of an estimation apparatus for subject tracking, as disclosed by Yoon in view of Grill, for the purposes of extracting specific information from images and improving efficiency (See Kim: [0049], [0003]).
Allowable Subject Matter
Claims 3-7 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.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 3, Yoon in view of Grill and Kim discloses the estimation apparatus according to claim 2 as applied above. However, neither Yoon nor any obvious combination of the closest known prior art discloses wherein the second reference image has a higher resolution than that of the first reference image.
Claims 4-6 are dependent on claim 3 and thus recite similarly allowable subject matter.
Regarding claim 7, Yoon in view of Grill discloses the estimation apparatus according to claim 1 as applied above. However, neither Yoon nor any obvious combination of the closest known prior art discloses wherein the processor is configured to: execute a first update process of updating the first reference image and the second reference image in a case where the selected model is switched from one of the first model or the second model to the other in the selection process.
Response to Arguments
Applicant's arguments filed 05/15/2026 have been fully considered but they are not persuasive.
Applicant asserts on page 10 that “based on the aforesaid rationale, claim 1 should thus be allowable and claims 10-11 should be allowable for the same rationale,” the rationale being that “claim 1 has been amended to further clarify the scope of the term ‘factor information’ by specifying that the factor information comprises ‘at least one of information related to the tracking subject or information related to imaging settings of the imaging element’”. Examiner disagrees. As stated above, amended claims 1, 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Yoon in view of Grill.
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 CAROLINE DEPALMA whose telephone number is (571)270-0769. The examiner can normally be reached Mon-Thurs 9:00am-4pm Eastern Time.
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/CAROLINE E. DEPALMA/Examiner, Art Unit 2675
/SJ Park/Primary Examiner, Art Unit 2675
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