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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 April 14, 2026 has been entered.
Allowable Subject Matter
Claims 1-2, 4-5, 7, 9-13 and 15 are allowed.
Reason for Allowance
The following is an examiner’s statement of reasons for allowance:
Claims 1-2, 4-5, 7, 9-13 and 15 are allowable over the prior art as the prior art references taken individually or in combination fail to particularly disclose, fairly suggest, or render obvious Applicant's independent claim language. The closest prior arts of record discloses a computer-implemented method for providing fingernail measurements, the computer-implemented method comprising: receiving, from a camera of a mobile device, first image data comprising a primary top-down view of at least one fingernail; receiving, from the camera, second image data comprising a secondary oblique view of a nail plate of the at least one fingernail, wherein the secondary oblique view is between a top-down view and a straight-on view of the nail plate and is captured during a same image capture session as the first image data; applying a trained machine learning model to automatically detect and segment the nail plate in both the first and second image data by classifying each pixel in the first and second image data as nail plate or background based on contextual image features; and determining a plurality of nail measurements from the 3D digital nail model. However, none of them alone or in any combination teaches generating, through a direct computational reconstruction from the segmented first and second image data, a three-dimensional (3D) digital nail model of the nail plate by: (i) assigning x and y coordinates to segmented nail surface pixels from the first image data, (ii) determining a nail height (h) to calculate z coordinates for the nail surface pixels based on the formula z = h - r + sqrt(r*r - (x - w/2 - 0.5)**2), and (iii) iteratively adjusting the nail height (h) and pose parameters comprising pitch, roll, and yaw angles relative to the camera until a projection similarity score between the 3D digital nail model and the segmented nail plate in the second image data exceeds a predetermined threshold as specified in claims 1.
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 14 is rejected under 35 U.S.C. 103 as being unpatentable over Gedamu (US Patent Pub. No.: US 2020/0135310 A1), hereinafter Gedamu, in view of Song (US Patent No.: US 11,147,353 B2), hereinafter Song.
Regarding claim 14, Gedamu teaches a computer-implemented method for providing fingernail measurements via a native mobile application running on a mobile device, the computer-implemented method comprising: receiving, via the native mobile application from a camera of the mobile device (In other embodiments, a single or plurality of sensors could be attached to a mobile phone, integrated within a mobile phone, or a combination thereof to enable a portable mobile version of the imaging device. [0060]), first image data [[of]]comprising a primary top-down view (Design III shows a scanning module with two sensors 144 of a first type secured to opposite ends of the frame and angled towards the scanning platform, and a third sensor 144' of a second type secured to the frame opposite the scanning platform (i.e., to capture the top-down view). [0058]. In some configurations, each sensor or each group of sensors can capture images individually, for example by having mutually exclusive fields of view, or by capturing images at different times. [0059]
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); receiving, via the native mobile application from the camera of the mobile device (In other embodiments, a single or plurality of sensors could be attached to a mobile phone, integrated within a mobile phone, or a combination thereof to enable a portable mobile version of the imaging device. [0060]), second image data comprising a secondary oblique view of a nail plate of the at least one fingernail captured during the image capture session (Design III shows a scanning module with two sensors 144 of a first type secured to opposite ends of the frame and angled towards the scanning platform (i.e., to capture the oblique view), and a third sensor 144' of a second type secured to the frame opposite the scanning platform. [0058]. In some configurations, each sensor or each group of sensors can capture images individually, for example by having mutually exclusive fields of view, or by capturing images at different times. [0059]
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); automatically segmenting the nail plate in the first and second image data (Next, a processing step involves segmenting and classifying the digital images in order to create exact digital 3D models of the user's nails, the 3D models preferably having the exact anatomical dimensions of the scanned nails. [0052]) using machine learning-based (Individual nail and feature (e.g. anatomical changes, color, thickness, splitting, concavity, pitting, ridges, dryness, and clubbing) models as well as population models based on demographics can be created using image processing software, machine learning, deep learning, and artificial intelligence (AI) and used to differentiate normal from abnormal nails and define the associated conditions (should they exist). [0085]) segmentation that classifies each pixel as nail plate or background (The step of segmentation and classification involves delineating the nails from all other objects in the image (i.e, the background), and classifying the nail by name (i.e. for the hand: thumb, index, middle, ring, and baby, for the feet: first, second, third, fourth, and fifth toe) and location (i.e. left or right side of the body). [0063]); generating a three-dimensional (3D) digital nail model directly fromsegmented first and second image data (Next, a processing step involves segmenting and classifying the digital images in order to create exact digital 3D models of the user's nails, the 3D models preferably having the exact anatomical dimensions of the scanned nails. [0052]) using an iterative optimization process (It is appreciated that the image processing module executes a suite of processing steps, including: segmentation and classification, digitization, smoothing, texturing, image optimization, design integration, space optimization and medical diagnostics. [0062]) that adjusts nail height and pose parameters comprising pitch, roll, and yaw (The step of digitization involves using the segmented images to create a digital model of the user's nails with the exact topology and anatomical boundaries thereof. A subsequent digital model can be created for artificial nails whose shape/dimensions conform to the topology and anatomical boundaries of the scanned nails. [0064])) to maximize similarity between the 3D model projection and the segmented nail plate in the second image data (Next, a processing step involves segmenting and classifying the digital images in order to create exact digital 3D models of the user's nails, the 3D models preferably having the exact anatomical dimensions of the scanned nails. [0052]).
Gedamu does not teach the following limitations as further recited, but Song further teaches a physical reference object positioned adjacent to the at least one fingernail during an image capture session (The model reconstruction apparatus using photogrammetry may include an image-receiving unit for receiving image data in which images of a nail of a target, for which an artificial nail model is to be generated, and a standard specification object are taken together. Abstract), wherein the physical reference object comprises a planar object having standardized physical dimensions (In any of the disclosed implementations of the second model reconstruction system, the standard specification object may be one of a credit card, a currency note, a coin, and a paper. Column 3 line 62); establishing a scale calibration for nail measurements based on the standardized physical dimensions of the physical reference object (The image-analyzing unit 120 may recognize the shapes of nail plates and nail tips of the nails of the target from the image data, and may determine parameters for the shapes of nail plates and the shapes of nail tips based on the standard specification object. Column 8 line 16) and an applied perspective transformation (Referring to FIG. 7, the image-analyzing unit 120 may set two points at positions corresponding to both sides of each nail from the second image in such a way as to set one point per position from a viewpoint in which the fingertips of the target are viewed, may set an additional point at at least one position on the nail tip, and may then determine the parameters using the two points and the additional point as coordinate values for the shape of the nail tip. Column 9 line 4), and determining a plurality of nail measurements from the 3D digital nail model and the established scale calibration (determining parameters for the shape of the nail plate and the shape of the nail tip based on the standard specification object, and a model-generating unit for generating an artificial nail model based on the parameters. Abstract).
It would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Gedamu to incorporate the teachings of Song to determine nail measurements from the 3D digital nail model and the established scale calibration using a physical reference object positioned adjacent to the fingernail during an image capture session in order to generate a user-customized artificial nail model that complies with existing standard specifications without requiring a complicated process or special tool.
Conclusion
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/LEI ZHAO/Examiner, Art Unit 2668
/VU LE/Supervisory Patent Examiner, Art Unit 2668
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