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 .
Election/Restrictions
Claims 8-10, 16 and 18 are withdrawn from further consideration as being drawn to a nonelected Species II. Election was made without traverse in the reply filed on 6/5/2026 and its made final.
Response to Arguments
Applicant’s arguments with respect to claims 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.
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, 2 and 6, 11-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US Pub. 2019/0266392) in view of Narita (US 10,839,529).
With respect to claim 1, Watanabe discloses An image processing apparatus comprising:
one or more processors that execute a program stored in a memory (see figure 1) and thereby function as:
a detection unit that detects a first part and a second part of a specific subject from an image, (see figure 2, numerical 231 and 232);
[a calculation unit that calculates a background movement amount and a movement amount of each of the first part and the second part between the image and a previous image;
an estimation unit that estimates a movement direction of the specific subject based on the background movement amount and the movement amount of each of the first part and the second part;] and
an association unit that, [based on a relationship between a position of the first part and a position of the second part specified by the estimated movement direction,] associates parts of the same subject, among the first part and the second part detected by the detection unit (see figure 2, numerical 240, and paragraph 0019, wherein … the matching determination unit 240 determines whether the object detected through the first detection unit 231 corresponds to the object detected through the second detection unit 232..), as claimed.
However, Watanabe fails to explicitly disclose a calculation unit that calculates a background movement amount and a movement amount of each of the first part and the second part between the image and a previous image;
an estimation unit that estimates a movement direction of the specific subject based on the background movement amount and the movement amount of each of the first part and the second part; and
an association unit that, based on a relationship between a position of the first part and a position of the second part specified by the estimated movement direction, as claimed.
Narita teaches a calculation unit that calculates a background movement amount and a movement amount of each of the first part and the second part between the image and a previous image; an estimation unit that estimates a movement direction of the specific subject based on the background movement amount and the movement amount of each of the first part and the second part, (see figure 3, wherein the subject and the background movement are calculated as shown, also see col. 12, lines 9-20, wherein …the motion vector detection unit 105 calculates a correlation value between the template area 701 and the search area 702, and determines a position in the correlation value calculation area 703 at which this value is the smallest. This makes it possible to specify a destination on the reference image, of the template area 701 that is on the base image. Also, it is possible to detect a motion vector “estimates a movement direction” whose direction and size are a direction and a moving amount toward the destination on the reference image that is based on the position of the template area on the base image); and
an association unit that, based on a relationship between a position of the first part and a position of the second part specified by the estimated movement direction, (see col. 13, lines 55-65, wherein …The clustering unit 1002 “a relationship” performs clustering processing on motion vectors obtained from the motion vector detection unit 105 …The “clustering processing” here refers to grouping, into one group, one or more motion vectors having similar directions and sizes (e.g., directions and sizes whose difference is smaller than or equal to a predetermined value) from among a plurality of motion vectors…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the two references as they are analogous because they are solving similar problem of object detection using image analysis, furthermore, both references are from same assignee as well. Teaching of Narita to calculate the movement and estimate the background and the subject can be incorporated in to the Watanabe system as suggested (see Abstract, wherein … based at least the detection result corrected by the correction unit), for suggestion, and modifying the system yields detection of the main subject (see Narita col. 2, lines 46-49), for motivation.
With respect to claim 2, combination of Watanabe and Narita further discloses wherein a positional relationship between the first part and the second part can be specified by the movement direction of the specific subject, and the association unit associates the first part and the second part so as to satisfy the positional relationship specified by the estimated movement direction, (see Watanabe paragraph 0029, wherein …For example, the matching determination unit 240 can determine that the same human is detected for only the head coordinates “positional relationship” with the highest likelihood of the object being a human…), as claimed.
With respect to claim 6, combination of Watanabe and Narita further discloses wherein the estimation unit estimates the movement direction based on a vector, among vectors indicating movement amounts for each of specific subjects each being the specific subject, which has a lowest movement amount in a vertical direction, (see Narita figure 3, Movement, and figure 10A-10D for the motion direction), as claimed.
With respect to claim 11, combination of Watanabe and Narita further discloses wherein the detection unit executes detection of the first part and detection of the second part separately, (see Watanabe figure 2, numerical 231 and 232 “detection unit executes detection of the first part and detection of the second part separately”), as claimed.
With respect to claim 12, combination of Watanabe and Narita discloses all the limitations as claimed and as rejected in claim 1, above. However, they fail to disclose wherein the detection unit detects the first part and the second part using a neural network in which is set dictionary data containing a combination of a type of the specific subject and a part to be detected, as claimed.
But, it is well known “Official Notice” in the art to use a neural network for detecting objects in a image (see US Pub. 2023/0386253, paragraph 0047). Therefore, It would have been obvious to one ordinary skilled in the art at the effective date of invention to simply utilize the conventional knowledge of using neural networks to attain the objects in an image in the Watanabe and Narita’s system to yields better and more accurate monitoring system, for motivation.
With respect to claim 13, combination of Watanabe and Narita further discloses wherein the specific subject is a human or an animal, (see Watanabe paragraph 0017, wherein …to detect a region presenting a characteristic shape of a human body…), as claimed.
With respect to claim 14, combination of Watanabe and Narita further discloses wherein the first part is a trunk and the second part is a head, (see Watanabe figure 6, head and body of a person read as trunk “body” and head), as claimed.
Claims 15 and 17 are rejected for the same reasons as set forth in the rejections of claim 1, because claims 15 and 17 are claiming subject matter of similar scope as claimed in claim 1.
Claims 3-5 are rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US Pub. 2019/0266392) in view of Narita (US 10,839,529) as applied to claim 1 above, and further in view of Baba (JP 2017212581, IDS document).
With respect to claim 3, combination of Watanabe and Narita discloses all the limitations as claimed and rejected in claim 1 above. However, they fail to explicitly disclose wherein the association unit associates the first part and the second part which satisfy the positional relationship specified by the estimated movement direction, and which are at a distance less than a threshold, as claimed.
Baba teaches wherein the association unit associates the first part and the second part which satisfy the positional relationship specified by the estimated movement direction, and which are at a distance less than a threshold, (see page 6 of translation, wherein …The associating unit 308 determines whether or not to associate the specific object region with the target object region according to the associating condition relating to the positional relationship between the target object region and the specific object region…; and …first condition is that the state in which the distance between the target object region and the specific object region is less than the distance threshold is continued during the determination period…), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of object detection using image analysis. Teaching of Baba to setup the conditions for the associating the two regions can be incorporated into the Watanabe and Narita system as suggested (see Watanabe Abstract, wherein … based at least the detection result), for suggestion, and modifying the system yields better tracing object (see Baba page 2 translation Technical filed), for motivation.
With respect to claims 4 and 5, combination of Watanabe, Narita and Baba for the same reasons of combining further discloses wherein when the estimation unit cannot estimate the movement direction, the association unit associates the first part and the second part which are at a distance less than a threshold; and wherein when the estimation unit cannot estimate the movement direction, the association unit skips associating the second part with the first part for which a plurality of the second parts at a distance less than the threshold are present, (see Baba translation page 6, detail for the second condition, also page 7 second paragraph), as claimed.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Watanabe (US Pub. 2019/0266392) in view of Narita (US 10,839,529) as applied to claim 1 above, and further in view of Automatic human posture estimation for sport activity recognition with robust body parts detection and entropy markov model, by Nadeem.
With respect to claim 7, combination of Watanabe and Narita discloses all the limitations as claimed and rejected in claim 1 above. However, they fail to explicitly disclose wherein when detecting the first part, the detection unit detects vectors indicating a position where the second part is highly probable to be present, and the association unit associates the first part for which is detected a vector, among the vectors having ending points within a search area set on the second part, which is consistent with the estimated movement direction, with the second part, as claimed.
Nadeem teaches wherein when detecting the first part, the detection unit detects vectors indicating a position where the second part is highly probable to be present (see section 3.2, key body parts detection, page 21473), and the association unit associates the first part for which is detected a vector, among the vectors having ending points within a search area set on the second part, which is consistent with the estimated movement direction, with the second part, (see page 21467, wherein …In this paper, we propose new robust body parts recognition model, automatic posture estimation and efficient multidimensional cues that reliably recognize complex human activities. Firstly, to extract human silhouettes from dynamic backgrounds, we acquire skin tone and saliency maps based on segmentations. Secondly, using the geometric aspects of the human body, the key body points of individual silhouettes are marked based on five core parts (head, hands and feet) along with seven additional key parts (torso, knees, shoulders and hips) of the body…; see section 3.2, page 21481, wherein …On the other hand, the current position of specific key body part movements is detected …), as claimed.
It would have been obvious to one ordinary skilled in the art at the effective date of invention to combine the references as they are analogous because they are solving similar problem of object detection using image analysis. Teaching of Nadeem to associate the body parts to one another using the movement and the various body parts “feature vectors” can be incorporated into Watanabe and Narita system as suggested (see Watanabe Abstract, wherein …the detection result), for suggestion, and modifying the system yields better the predictable results of matching the various parts of the body toe the same subject (see Nadeem Abstract), for motivation.
Conclusion
THIS ACTION IS MADE FINAL. 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 VIKKRAM BALI whose telephone number is (571)272-7415. The examiner can normally be reached Monday-Friday 7:00AM-3:00PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Gregory Morse can be reached at 571-272-3838. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/VIKKRAM BALI/ Primary Examiner, Art Unit 2663
Prosecution Insights