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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/19/2026 has been entered.
Claim Interpretation
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: model storage unit, characteristic point extraction unit, original matching degree calculation unit, target object detection unit, parameter setting unit, detection information storage unit, simple matching degree calculation unit in claim 1.
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.
Response to Arguments
Applicant's arguments filed 05/19/2026 have been fully considered but they are not persuasive.
Regarding amended claim 1, applicant argued that prior arts fail to teach matching degree calculation to be based on prior detection information, e.g., without acquisition of a new captured image when the detection parameter has been changed.
However, examiner respectfully disagrees. Despite of applicant’s argument, Mai discloses:
[0135] The method 440 then proceeds from the step 815 to a step 820, performed by the processor 1005 directed by the VIDD software 1033, which determines detectability 821 of each attribute in the image 120 of the candidate object, based on the viewing conditions including the relative orientation of the candidate object (e.g. bearing angle θ 541 in Equation (7)) determined at step 425 of method 400. The detectability 821 determined at this step serves as the term p(d.sub.i|a.sub.i, v) in computing the posterior probability using Equation (1). In one VIDD arrangement, the detectability is based on the performance of the classifiers used at the step 740 of the example of the method 430 for detecting attributes of the candidate object. The performance of an attribute classifier is determined by testing the classifier on a set of labelled test images of different objects with the said attribute, captured under a particular viewing condition v. Accordingly, the detectability of an attribute in a particular viewing condition can be determined based on the performance of an attribute classifier for the attribute, on a test set captured under said viewing condition. The detectability is then determined from the test results as follows: p(d=1|a=1, v) takes the value of the true positive rate of the attribute detector, p(d=0|a=1, v) takes the value of the false negative rate of the attribute detector, p(d=1|a=0, v) takes the value of the false positive rate of the attribute detector and finally p(d=0|a=0, v) takes the value of the true negative rate of the attribute detector. The above described test is repeated using sets of test images captured under all viewing conditions v of interest in order to fully characterize the detectability of each attribute. In one VIDD arrangement, the detectability of each attribute is pre-calculated during an offline training phase prior to executing method 400. In another VIDD arrangement, the detectability of each attribute is updated online during execution of method 400. In one example, the detectability is updated online based on feedback from a user about whether the object of interest has been correctly identified.
So, Mai does teach calculating detectability for the same attribute (detection information) with different/updated viewing condition (detection parameter changed).
Thus, rejection is proper and maintained.
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.
Claims 1-3 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over Mai et al. (US2018/0075300) in view of Odashima et al. (US2019/0340456).
To claim 1, Mai teaches an image processing device that detects a target object in a captured image of an imaging device (Fig. 3), the image processing device comprising:
a model storage unit that stores a model pattern (paragraph 0113, one or more template images of each attribute class);
a characteristic point extraction unit that extracts a characteristic point in which an attribute value is equal to or more than an extraction threshold (paragraph 0113, an attribute is classified by applying a predetermined threshold to features extracted from a region of the detected candidate, wherein equal to or more than would be an obvious condition for comparison), from the captured image (paragraphs 0057, 0059, 0113, 0130, features extracted from interest points on the candidate objects);
an original matching degree calculation unit that calculates a matching degree between the model pattern and an arrangement of the characteristic point (paragraph 0060, “detectability” of an attribute describes the degree of certainty with which the attribute can be detected in an image of a candidate object; paragraph 0113, an attribute is classified by computing a matching score between features of the detected candidate and one or more template images of each attribute class);
a target object detection unit that detects the target object in the captured image based on a comparison between the matching degree and a detection threshold (450 of Fig. 4; paragraph 0103, the object of interest is described by a predetermined plurality of attributes; paragraphs 0116-0118)
a parameter setting unit that sets a detection parameter including at least the detection threshold (Fig. 9, paragraph 0150, manually pre-defined probability thresholds for testing that the identity of the candidate);
a detection information storage unit that stores detection information including at least a position of the characteristic point with respect to the characteristic point of the target object detected by the target object detection unit (Fig. 5; paragraphs 0057, 0111, pixel location; paragraphs 0119-0123, candidate object location; paragraphs 0129, vector comprises position); and
a simple matching degree calculation unit that calculates, when the detection parameter has been changed, the matching degree based on the detection parameter changed and the detection information stored before the detection parameter is changed in the detection information storage unit (paragraphs 0060, 0113, “detectability” of an attribute describes the degree of certainty with which the attribute can be detected in an image of a candidate object; paragraph 0115, using the new camera settings to update the confidence that the candidate object is the object of interest; paragraph 0130, the attribute classifier is updated online while executing the method, for example based on feedback from a user about whether the object of interest has been correctly identified; paragraph 0135, the detectability of each attribute is updated online during execution of method. In one example, the detectability is updated online based on feedback from a user about whether the object of interest has been correctly identified; wherein obviously matching degree would change based on changed attributes, e.g., lighting condition, and stored detection information).
Mai explicitly teaches updating detectability/confidence and online updating of attribute classifiers in response to new camera settings or user feedback (paragraphs 0115, 0130, 0135). A person of ordinary skill would have been motivated to change detection parameters (for example, detection thresholds or camera settings) in view of Mai’s disclosure that detectability/confidence is updated to reflect changed imaging conditions, because adjusting such parameters is a routine and predictable optimization to maintain detection accuracy when imaging conditions (e.g., lighting or camera settings) change. Recalculating the matching degree based on stored detection information after a parameter change is a predictable and routine step: Mai teaches both (a) storage of detection information (e.g., pixel locations and candidate vectors) and (b) updating detectability/confidence based on new parameters or feedback, thus providing the necessary teachings to perform a recalculation (Fig. 8; paragraphs 0094-0095), which would have been obvious to a person of ordinary skill to recalculate the matching degree using the stored detection information upon changing detection parameters to maintain or improve detection performance.
But, Mai do not expressly disclose the extraction threshold.
Odashima teach extracting, a characteristic point, a point in which an attribute value is equal to or more than an extraction threshold, the detection parameter to be set by the parameter setting unit includes the extraction threshold (abstract, paragraphs 0005, 0046-0047, 0064-0068, 0097).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate teaching of Odashima into the apparatus of Mai, in order to implement comparison.
To claim 2, Mai and Odashima teach claim 1.
Mai and Odashima teach the detection information stored in the detection information storage unit includes the attribute value (as explained in response to claim 1 above).
To claim 3, Mai and Odashima teach claim 2.
Mai and Odashima teach wherein the attribute value includes at least any one selected from color, luminance, magnitude of a luminance gradient, and direction of the luminance gradient of the characteristic point (paragraph 0057, intensity gradient; paragraph 0059, body colour; paragraphs 0113, 0134-0135, lighting conditions).
To claim 5, Mai and Odashima teaches claim 1.
Though Mai and Odashima does not expressly disclose further comprising a user interface unit that displays the matching degree calculated by the simple matching degree calculation unit, displaying matching result is a well-known practice in the art, which would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate for preferential display, hence Official Notice is taken.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ZHIYU LU whose telephone number is (571)272-2837. The examiner can normally be reached Weekdays: 8:30AM - 5:00PM.
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, Stephen R Koziol can be reached at (408) 918-7630. 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.
ZHIYU . LU
Primary Examiner
Art Unit 2669
/ZHIYU LU/Primary Examiner, Art Unit 2665 June 4, 2026
Prosecution Insights