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 .
Notice to Applicant
Limitations appearing inside {} are intended to indicate the limitations not taught by said prior art(s)/combinations.
Claims 1-18 are currently pending.
Response to Amendments
The Amendment filled 01/05/2026 in response to Non-Final Office Action mailed 09/04/2025 has been entered, and Supplemental Amendment filed on 03/26/2026 in response to examiner initiated interview on 03/17/2026 has been entered. Claims 1-3, 4-6, 8-9, and 11-14 were amended (See Amendment 01/05/2026). Claims 15-18 are newly added(See Amendment 01/05/2026). Claims 1-2, 8-14, and 16 are amended (See Supplemental Amendment 03/26/2026)
As previously recorded in the interview summary of 01/08/2026, the objection to the Fig 4B has been withdrawn in light of the corrected drawing. The objection to claim 6 has been withdrawn in light of the amended claim. The rejections under 35 USC §112(b) have been withdrawn in light of the amended claims. The amended claims overcoming the rejections under 35 USC §103 have been withdrawn.
Response to Arguments/Remarks
Applicant’s arguments with respect to claims 1-14 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Information Disclosure Statement
No Information Disclosure Statement (IDS) was filed; therefore, no applicant-submitted references were considered.
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.
There is insufficient antecedent basis for the limitation s in the following claims.
Regarding Claim 1, it is unclear if the recitations of "the second size" of lines 11 and 15 refer to "a second size" in Claim 1 line 9 or 11. The same applies to recitations of "the second size" in:
Claim 2 line 3,
Claim 3 line 3,
Claim 4 line 5,
Claim 6 line 6.
Regarding claim 8, it is unclear if "the second size" of lines 13 and 14 refer to "a second size" in line 9 or line 10. The same applies to recitations of "the second size" in:
Claim 9 line 3,
Claim 18 line 2.
Regarding claim 11, it is unclear if "the second size" of line 12 refers to "a second size" in line 6 or line 7.
Regarding claim 12, it is unclear if "the second size" of line 12 refers to "a second size" in claim 11 line 6 or line 7.
Regarding claim 13, it is unclear if "the second size" of line 13 refers to "a second size" in claim 13 line 8 or line 9.
Regarding claim 14, it is unclear if "the second size" of line 13 refers to "a second size" in claim 14 line 8 or line 9.
Regarding claim 2, it is unclear if "the subject obtained in the first obtaining" in lines 2-3 refers to "a subject" of claim 1 line 5 or line 8.
Regarding claim 4, it is unclear if "the subject" in line 2 refers to "a subject" of claim 1 line 5 or line 8.
Regarding claim 5, it is unclear if "the subject obtained in the second obtaining" refers to "the subject" in claim 1 line 9 or "a subject" in claim 1 line 11.
Regarding claim 6, it is unclear if "the subject obtained in the first obtaining" in line 2 refers to "a subject" in claim 1 line 5 or line 8.
Regarding claim 6, is clear that "the subject are obtained from the area of interest of the first frame" refers to claim 1 line 5.
Regarding claim 9, it is unclear if "the subject" in line 3 refers to claim 8 line 9 or line 11.
Regarding claim 10, it is unclear if "the subject" in line 2 refers to claim 8 lines 5, 8, or 11.
Regarding claim 16, it is unclear if "the subject" in line 2 refers to claim 1 lines 9 or 12.
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, 4-14, and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over “Yamamoto” (Yamamoto et al., JP 2017016592 A), in view of “Okuda” (Okuda et al. JP 2020149642 A), previously cited in Office Action (09/04/2025).
1. (Currently Amended) An information processing apparatus comprising:
one or more hardware processors (Yamamoto, [0012]; The main object detection apparatus 100 may be configured as a single apparatus. In other words, the main subject detection device 100, CPU (CentralProcessingUnit), ROM (ReadOnlyMemory), RAM (RandomAccessMemory), provided with a hardware configuration); and
one or more memories storing one or more programs configured to be executed by the one or more hardware processors, the one or more programs including instructions for (Yamamoto, [0012]; CPU is by executing a program stored in the ROM or the HDD, for example, processing of each functional configuration and flowcharts to be described later is realized.):
first obtaining a first position of a subject by estimating the first position of the subject (Yamamoto, [0008]; a first detecting means for detecting a first candidate region of the main object; [0014]object detection unit 102 outputs information … position, size, color, etc.)
using a first estimation unit {trained} to estimate at least one of a first position and a first size of a subject from an area of interest of frames of a moving image (Yamamoto teaches the first detecting means searches for candidate over a plurality of frames therefore the first estimation unit is interpreted estimating a subject in frames of a moving image: [0014]; the object detection unit 102 searches a candidate uptake target object, to determine whether the candidate is reflected long over a plurality of frames; [0045]; in step S505, the same object area specifying unit 109, a candidate of the detected uptake target object in different frames (input images) makes a determination of whether the same object, identify the same object region);
second obtaining a second size of the subject in the first frame by estimating (Yamamoto, [0038] The second detection unit 106, by using the input feature amount map, and detects a region useful for detection results use function. ¶[0047]; in step S510, the second detecting unit 106, based on the feature amount map feature amount calculating unit has calculated, to detect a second candidate region),
using a second estimation unit, {trained} to estimate at least one of a second size and a second position of a subject in a still image (Yamamoto, [0047]; in step S 509, the feature amount calculating unit 105, the types of features determined in step S502, the detection target frame…creating a feature amount map. Then in step S510, the second detecting unit 106, based on the feature amount map feature amount calculating unit has calculated, to detect a second candidate region),
the second size being based on the first position obtained from the area of interest of the first frame in the first obtaining (Yamamoto, [0040] The final detector 107, a first candidate region which is input from the first detector 103, based on the second candidate area is input from the second detecting unit 106, as a main subject detection result, the final of the main object to detect the area); and
determining an area of interest in a second frame which follows the first frame by using the first position obtained in the first obtaining and the second size obtained in the second obtaining (Yamamoto teaches the area obtained with the final detection unit using both first and second detection unit outputs is used in detecting the candidate region (i.e., area of interest) in the next frame [0057]; In the step S701, the final detection unit 107, the detected region as the second candidate regions in the detection target frames, adds to the area of the capture object in the processing of the detection target frame in the next timing. Added uptake target object regions would be used in the detection of the first candidate region for the next detection target frame).
Yamamoto does not explicitly teach first estimation unit trained to estimate… frames of moving images, or a second estimation unit, trained to estimate … in a still image.
However, Okuda, a similar field of endeavor object tracking of a moving object, teaches first estimation unit trained (Okuda, [0092]; first sub-tracking unit 25…tracking is performed using a deep learning model such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), RSTM (Long Short-Term Memory), and a pattern recognition model such as SVM (Support Vector Machine). Deep learning models are trained.) … frames of moving images (Okuda, [0087]; first sub-tracking unit 25 is corrected by the position obtained based on the difference image between the frames (i.e., moving images)); and
using a second estimation unit, trained (Okuda, [0092], as shown above. second sub-tracking unit 26 may adopt a method other than the above as long as it is a motion detection method based on the difference. “May” is interpreted as both including and excluding a deep learning model, therefore the second tracking unit may also be a deep learning model.) … in a still image (Okuda [0040]; second sub-tracking unit 26 determines the center position of the tracking object in the current frame image (i.e., still image)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include trained estimation units as taught by Okuda to the invention of Yamamoto. The motivation to do so would be to optimize tracking of objects in situations where the background changes in a complicated manner, or background drift.
4. (Previously Presented) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 1. Yamamoto further teaches wherein, in a case where a first size of the subject obtained in the first obtaining is lower than or equal to a predetermined threshold, an area of interest in the second frame which follows the first frame is determined by using the first position and the first size obtained in the first obtaining and a second position of the subject and the second size obtained in the second obtaining (Yamamoto, [0039]; The detection target frame 401 is shown as in FIG. 4 (a), performs the feature quantity calculation suitable for detecting the movement of an object, by thresholding, of where the movement of the detection target frame 401 it is possible to identify whether a large area is present. Since only a single frame can not calculate the characteristic amount related to the motion, not only the detection target frame 401, the previous frame (not shown) also calculates a feature quantity of motion using. More specifically, for example, it calculates a difference between the detection target frame 401 and the previous frame, the threshold processing. FIG. 4 (b), the detection target frame 401 indicates a region 403, 404 identified as a large area of motion. The second detection unit 106, such searches a large area of motion, identify these regions (403, 404) as the second candidate region.).
5. (Previously Presented) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 1. Yamamoto further teaches wherein the first position obtained in the first obtaining and a second position of the subject obtained in the second obtaining are indicated by a similarity map showing a likelihood of a subject of each position (Yamamoto, [0040]; The final detector 107, as the main subject information, position and size of the main object area detected, further, outputs a score of the detection region (main subject likeness). The calculation of the main subject detection scores, position and size of the detection region, a correlation value obtained when detecting the first candidate region, a weighted the respective information of the feature values or the like obtained when detecting the second candidate region perform operations such as adding, it may be calculated.).
6. (Previously Presented) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 1. Yamamoto further teaches wherein in a case where the first position and a first size of the subject obtained in the first obtaining are not obtained from the area of interest of the first frame by the first estimation unit, an area of interest in the second frame which follows the first frame is determined by using the first position and the first size obtained in the first obtaining and a second position of the subject and the second size obtained in the second obtaining (Yamamoto, teaches a case in which the object enters the view at an angle that the camera is not able to follow there is a possibility that the object cannot be clearly distinguished it is suitable to use both the first and second candidate regions ([0068])
in a case where the first position and the first size of the subject are obtained from the area of interest of the first frame by the first estimation unit, the first position and the first size obtained in the first obtaining are determined as the second position and the second size of the area of interest in the second frame (Yamamoto teaches a case in which the camera as able to move and keep the target object within the view the first candidate region is taken as a final candidate region ([0069])).
7. (Original) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 1. Yamamoto further teaches wherein the one or more programs further include instructions for (Yamamoto, [0076]; the present invention provides a software (program) for realizing the functions of the above embodiments is supplied to a system or an apparatus via a network or various storage medium, a computer of the system or apparatus (or CPU or MPU) Program the read out is a process to be executed):
determining a position and a size of a subject in the second frame based on a feature amount to be obtained from the still image of the first frame and the area of interest in the second frame (Yamamoto teaches the output of the final detector (based on the first and second detectors) is the size and position of the area ([0040])).
8. (Currently Amended) Yamamoto teaches an image processing apparatus comprising:
one or more hardware processors (Yamamoto, [0012]; The main object detection apparatus 100 may be configured as a single apparatus. In other words, the main subject detection device 100, CPU (CentralProcessingUnit), ROM (ReadOnlyMemory), RAM (RandomAccessMemory), provided with a hardware configuration); and
one or more memories storing one or more programs configured to be executed by the one or more hardware processors, the one or more programs including instructions for (Yamamoto, [0012]; CPU is by executing a program stored in the ROM or the HDD, for example, processing of each functional configuration and flowcharts to be described later is realized.):
first obtaining a first position of a subject by estimating the first position of the subject from an area of interest of a first frame, using a first estimation unit {trained} to estimate at least one of a first size and a first position of a subject from an area of interest of frames of a moving image (Yamamoto teaches the first detecting means searches for candidate over a plurality of frames therefore the first estimation unit is interpreted estimating a subject in frames of a moving image: [0014]; the object detection unit 102 searches a candidate uptake target object, to determine whether the candidate is reflected long over a plurality of frames. [0045]; in step S505, the same object area specifying unit 109, a candidate of the detected uptake target object in different frames (input images) makes a determination of whether the same object, identify the same object region);
second obtaining a second size of the subject in the first frame by estimating (Yamamoto, [0038] The second detection unit 106, by using the input feature amount map, and detects a region useful for detection results use function. [0047]; in step S510, the second detecting unit 106, based on the feature amount map feature amount calculating unit has calculated, to detect a second candidate region), using a second estimation unit {trained} to estimate at least one of a second size and a second position of a subject in a still image (Yamamoto, [0047]; in step S 509, the feature amount calculating unit 105, the types of features determined in step S502, the detection target frame…creating a feature amount map. Then in step S510, the second detecting unit 106, based on the feature amount map feature amount calculating unit has calculated, to detect a second candidate region), the second size being based on the first position obtained from the area of interest of the first frame in the first obtaining (Yamamoto, [0040] The final detector 107, a first candidate region which is input from the first detector 103, based on the second candidate area is input from the second detecting unit 106, as a main subject detection result, the final of the main object to detect the area); and
Yamamoto does not explicitly teach first estimation unit trained to estimate… frames of moving images, or a second estimation unit, trained to estimate … in a still image.
Additionally, Yamamoto does not explicitly disclose determining the size and the position of the subject in the first frame by correcting the first position obtained in the first obtaining with the second size obtained in the second obtaining.
However, Okuda, a similar field of endeavor object tracking of a moving object, teaches first estimation unit trained (Okuda, [0092]; first sub-tracking unit 25…tracking is performed using a deep learning model such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), RSTM (Long Short-Term Memory), and a pattern recognition model such as SVM (Support Vector Machine). Deep learning models are trained.) … frames of moving images (Okuda, [0087]; first sub-tracking unit 25 is corrected by the position obtained based on the difference image between the frames (i.e., moving images)); and
using a second estimation unit, trained (Okuda, [0092], as shown above. second sub-tracking unit 26 may adopt a method other than the above as long as it is a motion detection method based on the difference. “May” is interpreted as both including and excluding a deep learning model, therefore the second tracking unit may also be a deep learning model.) … in a still image (Okuda [0040]; second sub-tracking unit 26 determines the center position of the tracking object in the current frame image (i.e., still image)).
However, Okuda, a similar field of endeavor object tracking of a moving object, teaches determining the size and the position of the subject in the first frame by correcting the first position obtained in the first obtaining with the second size obtained in the second obtaining (Okuda, [0041] The position correction unit 27 corrects the position of the object determined by the first sub-tracking unit 25 using the position of the object determined by the second sub-tracking unit 26, thereby correcting the center of the object in the current frame image).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include trained estimation units as taught by Okuda to the invention of Yamamoto. The motivation to do so would be to optimize tracking of objects in situations where the background changes in a complicated manner, or background drift.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include correcting the position taught by Okuda to the invention of Yamamoto. The motivation to do so would be to center the object in the current frame image.
9. (Currently Amended) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 8. Yamamoto further teaches wherein the first position and the first size of the subject in the first frame is determined as the second position of the subject and the second size obtained in the second obtaining (Yamamoto, [0062]; Although the first candidate region is present, when there is no second candidate region" is a case where a clue detection results use function information area is not detected. Such cases are, for example, if the automatic tracking is set, still to have an object such as waiting for the (animal, etc.) moves a scene is assumed as a result of detection use function. Since there is no motion between the object is still, the second candidate region is not detected.).
10. (Original) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 8. Yamamoto further teaches wherein the position of the subject is indicated by a similarity map showing a likelihood of a subject of each position (Yamamoto, [0040]; The final detector 107, as the main subject information, position and size of the main object area detected, further, outputs a score of the detection region (main subject likeness). The calculation of the main subject detection scores, position and size of the detection region, a correlation value obtained when detecting the first candidate region, a weighted the respective information of the feature values or the like obtained when detecting the second candidate region perform operations such as adding, it may be calculated.).
Claim 11 and 13 are similarly analyzed as analogous claim 1.
Claim 12 and 14 are similarly analyzed as analogous claim 8.
16. (Currently Amended) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 1 Okuda further teaches wherein the position of the area of interest in the second frame are determined by correcting the first position obtained in the first obtaining with second position of the subject obtained in the second obtaining (Okuda, [0041] The position correction unit 27 corrects the position of the object determined by the first sub-tracking unit 25 using the position of the object determined by the second sub-tracking unit 26, thereby correcting the center of the object in the current frame image.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include correcting the position taught by Okuda to the invention of Yamamoto. The motivation to do so would be to center the object in the current frame image.
17. (Previously Presented) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 16. Yamamoto further teaches wherein the position of the area of interest in the second frame are determined by not correcting the first position if a likelihood of a subject in a subject position is lower than a predetermined threshold (Yamamoto, [0038] states that the feature map amount is thresholded, and that the second candidate region exceeding the threshold is the detected as a region of the tracked object. This is interpreted as if it does not exceed the threshold then it is not used.).
Claims 2, 3, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto, in view of Okuda, and further in view of “Tanaka”(Tanaka, US 20220375104 A1).
2. (Currently Amended) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 1. The combination does not explicitly disclose wherein the size of the area of interest in the second frame are determined by correcting the first size of the subject obtained in the first obtaining with the second size obtained in the second obtaining.
However, Tanaka, a similar field of endeavor of continuously tracking an object even if the features of the object on images change, teaches wherein the size of the area of interest in the second frame are determined by correcting the first size of the subject obtained in the first obtaining with the second size obtained in the second obtaining (Tanaka, [0049]; tracking coordinates generation unit 158A calculates the average size and position of the associated correction coordinates as correction coordinates for one person, thereby generating tracking coordinates).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include correcting the position and size as taught by Tanaka to the combined invention of Yamamoto and Okuda. The motivation to do so would be to continue tracking the object if/when the features such as position or size change.
3. (Previously Presented) The combination of Yamamoto, Okuda, and Tanaka teach image processing apparatus according to claim 2. Yamamoto further teaches wherein a value, for which a weighted average of the first position and the first size obtained in the first obtaining and the second position and the second size obtained in the second obtaining is taken, is determined as the position and the size of the area of interest in the second frame (Yamamoto, [0040]; he calculation of the main subject detection scores, position and size of the detection region, a correlation value obtained when detecting the first candidate region, a weighted the respective information of the feature values or the like obtained when detecting the second candidate region perform operations such as adding, it may be calculated.).
15. (Previously Presented) The combination of Yamamoto, Okuda, and Tanaka teach image processing apparatus according to claim 2. Yamamoto further teaches wherein the size of the area of interest in the second frame are determined by not correcting the first size if a likelihood of a subject in a subject position is lower than a predetermined threshold (Yamamoto, [0038] states that the feature map amount is thresholded, and that the second candidate region exceeding the threshold is the detected as a region of the tracked object. This is interpreted as if it does not exceed the threshold then it is not used.).
Claim 18 is rejected under 35 U.S.C. 103 as being unpatentable over Yamamoto in view of Okuda and further in view of “Matsubara” (Matsubara, JP 2012085090 A).
18. (Previously Presented) The combination of Yamamoto and Okuda teaches the image processing apparatus according to claim 8. The combination does not explicitly disclose wherein the size of the subject in the first frame is determined as the second size in the second obtaining, and the position of the subject in the first frame is determined as the first position in the first obtaining
However, Matsubara, a similar field of endeavor of tracking a subject to be tracked in a plurality of image data obtained in time series, teaches wherein the size of the subject in the first frame is determined as the second size in the second obtaining, and the position of the subject in the first frame is determined as the first position in the first obtaining (Matsubara, [0038]; step S50, the size detection unit 18 detects the size of the subject for each subject detected in step S40 in the nth frame. [0039] S60, the position detection unit 16 detects the position of the subject for each subject detected in step S40 in the nth frame).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include using the size of the second obtaining as taught by Matsubara to the combined invention of Yamamoto and Okuda. The motivation to do so would be because even when a subject other than the tracking target is detected, it is not necessary to obtain the size of the subject other than the tracking target.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include using the position of the first obtaining as taught by Matsubara to the combined invention of Yamamoto and Okuda. The motivation to do so would be because even when a subject other than the tracking target is detected, there is no need to obtain the position of the subject other than the tracking target.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kondo et al. (US 20090175496 A1), teaches object tracking of a moving face and changing the tracking point in subsequent frames as needed.
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 CHANDHANA PEDAPATI whose telephone number is (571)272-5325. The examiner can normally be reached M-F 8:30am-6pm (ET).
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/CHANDHANA PEDAPATI/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669
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