DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application is being examined under the pre-AIA first to invent provisions.
2. This is in response to the applicant response filed on 08/05/2026. In the applicant’s response, claims 1, 3, 7-11, 13, and 17-20 were amended; claims 4-6 and 14-16 were cancelled; claims 21-26 were newly added. Accordingly, claims 1-3. 7-13, and 17-26 are pending and being examined. Claims 1 and 11 are independent form.
Claim Rejections - 35 USC § 101
3. The claim rejections under 35 USC § 101 in the previous office action are withdrawn in view of applicant’s amendment.
Claim Rejections - 35 USC § 103
4. 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 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.
5. The following is a quotation of pre-AIA 35 U.S.C. 103(a) which forms the basis for all obviousness rejections set forth in this Office action:
(a) A patent may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
6. Claims 1-2, 9-12, and 19-26 are rejected under pre-AIA 35 U.S.C. 103(a), as being unpatentable over Matsugu et al (US 2007/0025722, hereinafter “Matsugu”) in view of Yan (US6,975,750, hereinafter “Yan”).
Regarding claim 1, Matsugu discloses a computer-implemented method (the image capturing apparatus and the method, see abstract and fig.3), comprising:
acquiring, using an imaging device, a plurality of images (see S1 of fig.3 and para.70: “it [sequentially] acquires an image via the image input unit 2.”), each image of the plurality of images comprising a group of pixels corresponding a face (the smile face image detection, see fig.18A-18D);
tracking the face within the plurality of images; for at least one subset of images within the plurality of images: determining, using at least one classifier, a feature classification for the subset of images (wherein the expression change of the each of the several face frames is checked to see whether it meets the condition of the smiling face classification; see S71-S72 of fig.7 and para.99-para.101; also see fig.18A-18D and para.183);
see S71 of fig.7 and para.100, lines 8-12: wherein the image capture apparatus estimates the “errors between time-series data of m feature quantities in each of several frames from the past to the present, and time-series data of corresponding feature quantities of a facial expression registered as model data.” See S71 of fig.7 and para.101. And then, “the state change estimation unit 4 estimates the time until the facial expression reaches a specific one (e.g., smile),”, e.g., “the state change estimation unit 4 estimates the time when the error vector sequence converges to the zero vector”. In other words, when the facial expression error/distance between the current facial expression and the desire smile facial expression is near to zero, the current facial expression is a smile state); and
initiating a set of one or more operations based at least in part on the feature decision (see para.109: “The time when the value becomes equal to or smaller than a threshold (the facial expression converges to a specific one) is predicted as the best facial expression time.” And then, “the image data corresponding to the estimated timing is stored and controlled”; see para.128.).
As explained above, the mere difference is that Matsugu does not explicitly discloses “training the at least one classifier based, at least in part, on a pose of the face and an illumination condition of the face depicted in the at least one subset of images” as recited by claim 1. However, in the same field of endeavor, Yan teaches this element. See fig.9 and col. 12, lines 1-11, to train a face recognition classifier, Yan teaches a method which can synthesize numerous training face images at various poses and various illuminations from one real front face image. It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate the teachings of Yan into the teachings of Matsugu and train a face recognition classifier using numerous face images obtained at various poses and various illuminations. Suggestion or motivation for doing so would have been to recognize subjects regardless of the pose postures and illumination characteristics associated with an image as taught by Yan, see Abstract. Therefore, the claim is obvious over Matsugu in view of Yan.
Regarding claim 2, 12, the combination of Matsugu and Yan discloses, further comprising applying face recognition to the face (Matsugu, the smile face image detection, see fig.18A-18D).
Regarding claim 9, 19, the combination of Matsugu and Yan discloses, wherein the one or more operations comprises capturing an image feature (Matsugu, the smile face image capturing, see fig.18A-18D)
Regarding claim 10, 20, the combination of Matsugu and Yan discloses the computer-implemented method of claim 1, further comprising: determining a plurality of feature decisions corresponding to a plurality of faces; and wherein the one or more operations comprises capturing an image based on the plurality of feature decision being satisfied for a threshold number of faces within the plurality of faces (Matsugu, see pqara.112: “Particularly in this mode, the photographing time (image input timing) must be so controlled as to satisfy requirements on several facial expressions such that a plurality of objects open their eyes (do not close their eyes), close their mouths (or smile), and face the front.”).
Regarding claim 11, claim 11 is an inherent variation of claim 1, and thus it is explained and rejected for the reasons set forth in the rejection of claim 1.
Regarding claim 21, 24, the combination of Matsugu and Yan discloses, wherein the feature decision is made based on a sign and an absolute value of the confidence parameter (Matsugu, see para.73: “In step S4, the state change estimation unit 4 predicts the time (best frame timing) when the detected facial expression of the principal object changes to a predetermined one (e.g., smile) corresponding to the image capturing mode.”).
Regarding claim 22, 25, the combination of Matsugu and Yan discloses, wherein capturing the image is performed when the feature decision corresponds to at least one particular feature having a predetermined level of prevalence in the at least one subset of images (Matsugu, see para.73: “In step S4, the state change estimation unit 4 predicts the time (best frame timing) when the detected facial expression of the principal object changes to a predetermined one (e.g., smile) corresponding to the image capturing mode.”).
Regarding claim 23, 26, the combination of Matsugu and Yan discloses, wherein the at least one classifier comprises a binary classifier (this feature is obvious for one of ordinary skill in the art since Matsugu discloses “when the detected facial expression of the principal object changes to a predetermined one (e.g., smile) corresponding to the image capturing mode”. In other words, when the detected facial expression of the principal object changes to a blink state, the classifier value can set to zero while when the detected facial expression of the principal object changes to a smile state, the classifier value can set to one.).
7. Claims 3 and 13 are rejected under pre-AIA 35 U.S.C. 103(a), as being unpatentable over Matsugu in view of Yan and further in view of Kamei (US2005/0105779, hereinafter “Kamei”).
Regarding claim 3, 13, the combination of Matsugu and Yan does not disclose, wherein training the at least one classifier is performed using a Two-Dimensional Discrete Cosine Transform (2DCT). However, in the same field of endeavor, Kamei teaches this element. See para.13, to extract features from a face image for face recognition, Kamei teaches a method which applies a discrete cosine transform to face images for extracting features. It would have been obvious to one of ordinary skill in the art at the time the invention was made to incorporate the teachings of Kamei into the teachings of the combination of Matsugu and Yan and utilize a discrete cosine transform to obtain face features. Suggestion or motivation for doing so would have been to “extract the face features from local areas cut out in different positions, thus creating face features as face meta-data” as taught by Kamei, see Abstract. Therefore, the claim is obvious over Matsugu in view of Yan and further in view of Kamei.
8. Claims 7-8 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Matsugu in view of Yan and further in view of Viola et al (“Rapid Object Detection using a Boosted Cascade of Simple Features”, 2001, hereinafter “Viola”).
Regarding claim 7, 17, the combination of Matsugu and Yan discloses, wherein the feature classification comprises at least one selected from the group consisting of Haar feature. However, this element is well-known and widely used in the field of facial expression detection in images. As evidence, Viola teaches a method for detecting facial regions by using Haar wavelets and generating the so-called Haar-like features. See Sec.1, para.3. It would have been obvious to one of ordinary skill in the art before the effective filling date of the claimed invention was made to incorporate the teachings of Viola into the teachings of the combination of Matsugu and Yan and extract the Haar feature from images for face expression classification. Suggestion or motivation for doing so would have been to determine whether an image is a facial image. Viola, see Abstract, and the claim is unpatentable.
Regarding claim 8, 18, the combination of Matsugu, Yan, and Viola discloses, wherein the feature classification is the smile feature and determining the feature decision further comprises thresholding the feature decision such that the feature decision is selected from the group consisting of a smile, no smile, and inconclusive (Matsugu, see para.109: “The time when the value becomes equal to or smaller than a threshold (the facial expression converges to a specific one) is predicted as the best facial expression time.” And then, “the image data corresponding to the estimated timing is stored and controlled”; see para.128. Also see pqara.112: “Particularly in this mode, the photographing time (image input timing) must be so controlled as to satisfy requirements on several facial expressions such that a plurality of objects open their eyes (do not close their eyes), close their mouths (or smile), and face the front.”).
Response to Arguments
9. Applicant's arguments have been considered but are moot in view of the new ground(s) of rejection.
Conclusion
10. 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 extension fee 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 date of this final action.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to RUIPING LI whose telephone number is (571)270-3376. The examiner can normally be reached 8:30am--5:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, HENOK SHIFERAW can be reached on (571)272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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