DETAILED ACTION
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims do not fall within at least one of the four categories of patent eligible subject matter because "A program” in claim 10 defines a computer program, embodying functional descriptive material. However, the claim does not define a non-transitory computer-readable medium or memory and is thus non-statutory for that reason. That is, the scope of the presently claimed computer program can range from paper on which the program is written, to a program simply contemplated and memorized by a person. The examiner suggests amending the claim to embody the program on “non-transitory computer-readable medium” or equivalent in order to make the claim statutory. Any amendment to the claim should be commensurate with its corresponding disclosure.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-3, 5, 9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kaneda (US 20100189358)
Regarding claim 1, Kaneda teaches a facial-type diagnostic apparatus, comprising:
an image acquirer that acquires a first image and a second image, the first image being a captured image of a first facial expression of a user(S308 in Fig. 3, which is based on S213 in Fig. 2), the second image being a captured image of a second facial expression of the user(S300-S306 in Fig. 3, S800 in Fig. 15, which is based on S215 in Fig. 2);
a feature value extractor that extracts a first feature value of a facial part of the user in the first image and a second feature value of the facial part of the user in the second image ( S807 in Fig. 15); and
a facial type determiner that determines a facial type of the user based on the first feature value and the second feature value( S808 in Fig. 15) .
Regarding claim 2, Kaneda teaches the facial-type diagnostic apparatus according to claim 1, wherein the facial type determiner determines the facial type by comparing a predetermined criterion and a result acquired by performing a calculation using the first feature value and the second feature value ([0121], the features and the expression is determined to be a smiling facial expression if the SumScore is not less than a prescribed score) .
Regarding claim 3, Kaneda teaches the facial-type diagnostic apparatus according to claim 2, wherein the facial type determiner performs the calculation by assigning a weight to the second feature value, the weight being set in accordance with a degree of influence due to a facial expression change for each of a plurality of the facial parts( [0121], compute the degree of smiling facial expression from the weight and variation v1, v2, and v3 of the features).
Regarding claim 5, Kaneda teaches the facial-type diagnostic apparatus according to claim 2, wherein: the second facial expression is classified into a plurality of facial expression levels, and the facial type determiner assigns a weight depending on the plurality of facial expression levels to the second feature value and executes the calculation( [0121], compute the degree of smiling facial expression from the weight and variation v1, v2, and v3 of the features) .
claim 9 recites similar subject matter as in claim 1, and is also rejected.
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.
Claim(s) 4, 6-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kaneda.
Regarding claim 4, Kaneda teaches the facial-type diagnostic apparatus according to claim 2, wherein:
the image acquirer acquires a video of the user of a predetermined period of time([134], the embodiment is equally applicable to video cameras).
Kaneda does not expressly teach
the feature value extractor calculates a frequency at which the second facial expression of the user is detected in the video, and
the facial type determiner executes the calculation by assigning a weight corresponding to the frequency to the second feature value.
However, Kaneda teaches
calculates a frequency at which the second facial expression of the user([0146], accumulating a large quantity of neutral state image data and image data for each facial expression to be determined), and
the facial type determiner executes the calculation by assigning a weight corresponding to the frequency to the second feature value([0146], computing six filter differential output values based on these image data ) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use different frame images in the video as the neutral and expression images in training the expression determination model, with motivation “to determine the facial expression consists in using a neural network” ( [0146]).
Regarding claim 6, Kaneda teaches the facial-type diagnostic apparatus according to claim 1.
Kaneda does not expressly teach, in the embodiment that teaches the limitations in claim 1, wherein the facial type determiner further calculates a contribution degree indicating a proportion of contribution of one or both of the first facial expression and the second facial expression to determination of the facial type
However, Kanea does teach in another embodiment
the facial type determiner further calculates a contribution degree indicating a proportion of contribution of one or both of the first facial expression and the second facial expression to determination of the facial type([0141], As for the function used to compute the degree of neutrality, a large quantity of neutral state images is subjected to horizontal and vertical edge detection and the degree is computed from statistical data concerning the sum of the respective filter output values) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use different expression determination methods, with motivation that “using a neural network… many neurons may be utilized for determining smiling facial expression, facial expression of closed eyes, crying facial expression and the like, and imaging is inhibited when facial expression of closed eyes, crying facial expression or the like is detected as well as execution of imaging when smiling facial expression is detected”) ( [0146]).
Regarding claim 7, Kaneda teaches the facial-type diagnostic apparatus according to claim 1.
Kaneda does not expressly teach, in the embodiment that teaches the limitations in claim 1, wherein the first facial expression is a neutral face, and the second facial expression is a smiling face.
However, Kanea does teach in another embodiment
the facial type determiner further calculates a contribution degree indicating a proportion of contribution of one or both of the first facial expression and the second facial expression to determination of the facial type([0141], As for the function used to compute the degree of neutrality, a large quantity of neutral state images is subjected to horizontal and vertical edge detection and the degree is computed from statistical data concerning the sum of the respective filter output values) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use different expression determination methods, with motivation that “using a neural network… many neurons may be utilized for determining smiling facial expression, facial expression of closed eyes, crying facial expression and the like, and imaging is inhibited when facial expression of closed eyes, crying facial expression or the like is detected as well as execution of imaging when smiling facial expression is detected”) ( [0146]).
Regarding claim 8, Kaneda teaches the facial-type diagnostic apparatus according to claim 7, wherein the facial type includes four types classified in accordance with a first determination criterion and a second determination criterion, the first determination criterion being based on a face shape, an eye position, a nose length, a mouth size([0157],opening the mouse), a distance from eyes to a lip, and a distance between the eyes, the second determination criterion being based on the face shape, an eye shape([0147], facial expression of closed eyes), and a lip thickness.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JIANGENG SUN whose telephone number is (571)272-3712. The examiner can normally be reached 8am to 5pm, EST, M-F.
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JIANGENG SUN
Examiner
Art Unit 2661
/Jiangeng Sun/Examiner, Art Unit 2671
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