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 .
This office action is in response to communication filed on 1/15/2026. Claims 17-28 are pending on this application.
Response to Arguments
Applicant’s arguments with respect to claim(s) 17 and 18 have been considered but are moot in view of the new grounds of rejection.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a position coordinate calculation unit in claims 17 and 18; a reliability calculation unit in claim 19 .
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claims 25 and 26 recite the limitation " The localization method according to claim 19". There is insufficient antecedent basis for this limitation in the claim because claim 19 recites a localization apparatus.
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.
Claim(s) 17, 18, 22-24, 27, and 28 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kraft et al (US20170140245) in view of Tompson et al ("Efficient object localization using convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, pages 648-656, retrieved from the Internet on 8/18/2025) and Papandreou et al ("Towards accurate multi-person pose estimation in the wild." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, pages 4903-4911, retrieved from the Internet on 1/28/2025).
Regarding claim 17, Kraft teaches a localization apparatus comprising:
a deep learning model (103, 104, and 106 in figs. 2 and 4) for semantic segmentation (para. [0025]-[0026], [0041]) trained by using a plurality of combinations of training image data (401 in fig. 4) in which position coordinates desired to be detected on an image obtained by imaging a subject is specified (para. [0054] centroid of each blob) and teacher image data in which a pixel group representing a circular or polygonal shape centered on the position coordinates desired to be detected, which is configured with the same pixel value, is at a position relative to the subject at the position coordinates desired to be detected on the combined training image data (par. [0063]); and
a position coordinate calculation unit calculating position coordinates desired to be obtained in an image of a new subject by using inference image data obtained by inputting the image of the new subject of which the position coordinates are desired to be obtained to the deep learning model (para. [0040], the vehicular path detection module 105 shown in FIG. 2 may analyze an image to create a probabilistic heat map or blocked image containing an area of interest of vehicular paths where moving vehicles are expected).
Kraft fails to teach wherein the position coordinate calculation unit includes a process of obtaining a connected component of the inference image data and a center of gravity of the connected component.
However Tompson teaches a process of obtaining a connected component of the inference image data and a center of gravity of the connected component (section 3.3, first and second paragraphs).
Therefore taking the combined teachings of Kraft and Tompson as a whole, it would have been obvious to incorporate the features of Tompson into the apparatus of Kraft. The motivation to combine Tompson and Kraft would be to improve generalization performance (section 3.3 of Tompson).
Kraft also fails to teach wherein a pixel group representing a circular or polygonal shape centered on the position coordinates desired to be detected is formed on a uniformly labeled image as a background.
However Papandreou teaches forming a pixel group representing a circular shape (fig. 3, disk-shaped heat maps) on a uniformly labeled image as a background (page 4907 left side second paragraph, The training target is a map of zeros and ones. The pixels inside the disk are interpreted to have a value one and any value of zero is interpreted to be the background; page 4907 right side first paragraph).
Therefore taking the combined teachings of Kraft and Tompson as a whole, it would have been obvious to incorporate the features of Tompson into the apparatus of Kraft. The motivation to combine Tompson and Kraft would be to greatly improve average precision (page 4904 left side first paragraph).
Regarding claim 18, the claim recites similar subject matter as claim 17 and is rejected for the same reasons as stated above.
Regarding claim 22, the claim recites similar subject matter as claim 17 and is rejected for the same reasons as stated above.
Regarding claim 23, the claim recites similar subject matter as claim 17 and is rejected for the same reasons as stated above.
Regarding claim 24, the modified invention of Kraft teaches a localization method claim 18, wherein a pixel group different from background of the subject with respect to the subject at the position desired to be detected in the teacher image data is a circle or a polygon centered on the position coordinates desired to be detected, which is configured with the same pixel value (par. [0063] of Kraft),
the deep learning model is a deep learning network for semantic segmentation (para. [0025]-[0026], [0041] of Kraft), and
the position coordinate calculation unit includes a process of obtaining a connected component of the inference image data and a center of gravity of the connected component (section 3.3 of Tompson, first and second paragraphs).
Regarding claim 27, the claim recites similar subject matter as claim 18 and is rejected for the same reasons as stated above.
Regarding claim 28, the claim recites similar subject matter as claim 18 and is rejected for the same reasons as stated above.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kraft et al (US20170140245), Tompson et al ("Efficient object localization using convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015, pages 648-656, retrieved from the Internet on 8/18/2025), and Papandreou et al ("Towards accurate multi-person pose estimation in the wild." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, pages 4903-4911, retrieved from the Internet on 1/28/2025) in view of Ahmadyan et al (US20220191542).
Regarding claim 19, the modified invention of Kraft fails to teach a localization apparatus further comprising a reliability calculation unit calculating reliability of the position coordinates calculated by the position coordinate calculation unit by using information about the pixel group of the inference image data output from the deep learning model.
However Ahmadyan teaches calculating reliability of the position coordinates by using information about a pixel group of an inference image data output from the deep learning model (para. [0120], a heat map containing a probability distribution that represents a position of the object within the first image. Based on the heat map, a peak pixel of the heat map may be selected to represent a center of the object; para. [0121]).
Therefore taking the combined teachings of Kraft, Papandreou and Tompson with Ahmadyan as a whole, it would have been obvious to incorporate the features of Ahmadyan into the apparatus of Kraft, Papandreou and Tompson. The motivation to combine Ahmadyan, Papandreou, Tompson and Kraft would be to allow for the determination and tracking of pose data from images in a manner that may be improved (para. [0051] of Ahmadyan).
Allowable Subject Matter
Claims 20-21 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.
Claims 25 and 26 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
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 LEON VIET Q NGUYEN whose telephone number is (571)270-1185. The examiner can normally be reached Mon-Fri 11AM-7PM.
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.
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/LEON VIET Q NGUYEN/Primary Examiner, Art Unit 2663