DETAILED ACTION
Notice of Pre-AIA or AIA Status.
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. Claims 1-18 filed on 08/30/2024 are pending and being examined. Claims 1, 17, and 18 are independent form.
Priority
3. Acknowledgment is made of applicant's claim for PCT priority under 35 U.S.C. 371, where the benefit of foreign priority was further claimed.
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 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 of this title, 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.
6. Claims 1-9 and 11-18 are rejected under 35 U.S.C. 103 as being unpatentable over Esbech et al (US 2017/0165038, hereinafter “Esbech”) in view of Kim et al (“A Digital Shade-Matching Device for Dental Color Determination Using the Support Vector Machine Algorithm”, 2018, hereinafter “Kim”).
Regarding claim 1, Esbech discloses a method performed by an electronic device (the method and the system for use in determining shade of a patient's tooth; see abstract), the method comprising:
generating a three-dimensional image of an object based on a two-dimensional image set of the object obtained from an intraoral scanner, the object including a plurality of unit objects (see 102 of fig.1 and para.139: “a series of sub-scans of the patient's set of teeth is recorded, where a plurality of said sub-scans comprises both texture information and shape information for the tooth [selected by the user]”; see para.123: “the sub-scans are recorded using an intra-oral scanner”; see para.160: wherein each sub-scan includes “a sequence of 2D images [for the tooth]”);
based on a user's selection for a first point of the three-dimensional image, extracting a first color information set corresponding to a first region including the first point (see para.161: “[a] digital 3D representation [i.e., L*a*b*] of the tooth can be generated by combining sub-scans [including includes a sequence of 2D images] acquired from different orientations relative to the teeth”; also see 103 of fig.1 and para.140);
using the Euclidean Distance model configured to output a color similarity based on an input of the first color information set; and determining representative color information of a first unit object corresponding to the first point based on a magnitude of the color similarity (calculating the Euclidean distance (i.e., the similarity) between the input color information (
L
p
*
,
a
p
*
,
b
p
*
) generated by combining the sequence of 2D images and the reference tooth shade value (
L
R
i
*
,
a
R
i
*
,
b
R
i
*
) and selecting the smallest reference/representative shade tab; see para.174.).
As explained above, the mere difference is, Esbech does not disclose “using an artificial intelligence model configured to output a color similarity based on an input of the first color information set” but instead discloses using the Euclidean Distance model configured to that. However, in the same field of endeavor, Kim teaches this. Specifically, Kim teaches, using a trained SVM (i.e., an artificial intelligence) model configured to output a most similar to one of the natural tooth colors based the input color information extracted from a tooth. For instance, as disclosed by fig. 7(a) and the corresponding paragraphs, wherein the “Test 3L2.5” is the input color information extracted from a tooth, 3L2.5 and 3R2.5 are two shade tabs and respectively represent the two different natural tooth colors, the trained SVM classifies the input Test 3L2.5 into the 3R2.5 shade tab because it is the most similar to 3R2.5. 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 Kim into the teachings of Esbech and replace the Euclidean Distance model taught by Esbech with the SVM taught by Kim. Suggestion or motivation for doing so would have been to increase the accuracy of the digital she-matching method/device as taught by Kim, see Table 1. Therefore, the combination of Esbech and Kim suggests or teaches all the limitations recited in claim 1, and the claim is unpatentable over Esbech in view of Kim.
Regarding claim 2, the combination of Esbech and Kim discloses the method according to claim 1, further comprising transmitting the representative color information of the first unit object to an external terminal of the electronic device (Esbech, see 648-649 in fig.6 and para.156: “...comprise a unit 648 for transmitting a digital restoration design and a CAD model of a milling block to e.g. a computer aided manufacturing (CAM) device 649 for manufacturing a shaded dental restoration or to another computer system e.g. located at a milling center where the dental restoration is manufactured.”).
Regarding claim 3, the combination of Esbech and Kim discloses the method according to claim 2, wherein the transmitting representative color information to the external terminal comprises transmitting information about the first region including the first point to the external terminal (ibid.).
Regarding claim 4, the combination of Esbech and Kim discloses the method according to claim 1, wherein the extracting the first color information set corresponding to the first region comprises determining the first region including a plurality of points within a reference distance from the first point (Esbech, see the equation disclosed para. 162, and the equation disclosed in para.174.
Regarding claim 5, the combination of Esbech and Kim discloses the method according to claim 1, wherein the three-dimensional image includes a valid region and an exceptional region (Esbech, see the tooth regions 212 and 213, the soft tissue region 214 in fig.2), and wherein the extracting the first color information set corresponding to the first region comprises, if the first region includes the exceptional region, extracting the first color information set corresponding to the first region with the exceptional region excluded (Esbech, see para. 144, lines 31-39, the system may reject the shade value in the soft tissue region 214).
Regarding claim 6, the combination of Esbech and Kim discloses the method according to claim 1, wherein the extracting the first color information set corresponding to the first region comprises: identifying a first two-dimensional image set corresponding to at least some of a plurality of points included in the first region, the first two-dimensional image set being a sub-set of the two-dimensional image set; and extracting a color code of a pixel of a two-dimensional image included in the first two-dimensional image set (Esbech, see 102 of fig.1 and para.139: “a series of sub-scans of the patient's set of teeth is recorded, where a plurality of said sub-scans comprises both texture information and shape information for the tooth.”).
Regarding claim 7, the combination of Esbech and Kim discloses the method according to claim 6, wherein the first two-dimensional image set includes only two-dimensional images used to generate the three-dimensional image (ibid.).
Regarding claim 8, the combination of Esbech and Kim discloses the method according to claim 6, wherein the extracting the color code of the pixel of the two-dimensional image included in the first two-dimensional image set comprises: determining an extraction order of two-dimensional images included in the first two-dimensional image set based on a scan order; and extracting the color code of the pixel of the two-dimensional image included in the first two-dimensional image set based on the extraction order (Esbech, calculating the average color value based on the ordered N scans; see para.162).
Regarding claim 9, the combination of Esbech and Kim discloses the method according to claim 6, wherein the first color information set includes one or more pieces of first color information having a reference size, and wherein the extracting the first color information set corresponding to the first region further comprises generating the first color information based on the reference size (ibid.).
Regarding claim 11, the combination of Esbech and Kim discloses the method according to claim 1, wherein the first color information set includes N pieces (where N is a natural number) of first color information (Kim, see the 26 color tabs shown by fig.6 and Sec.3; see fig.6 and Sec.3, par.1), and wherein the using the artificial intelligence model comprises: calculating sub-color similarities between respective reference colors included in a reference color group and the respective ones of the N pieces of first color information included in the first color information set; and calculating the color similarity based on the N sub-color similarities (Kim, see fig.7 (a); wherein the input Test 3L2.5 is classified into the 3R2.5 shade tab because it is the most similar to 3R2.5).
Regarding claim 12, the combination of Esbech and Kim discloses the method according to claim 1, wherein learning data of the artificial intelligence model includes a pair of a first label indicating a first reference color included in a reference color group and a first scan result of a first model corresponding to the first reference color (Kim, wherein the classification model SVM was trained by the training sets each of which has an input scan result and its corresponding reference color label; see fig.5, par.1 and par.2 on page 7).
Regarding claim 13, the combination of Esbech and Kim discloses the method according to claim 12, wherein the learning data of the artificial intelligence model further includes a pair of the first label and a second scan result of the first model, and wherein second scan conditions of the second scan result are at least partially different from first scan conditions of the first scan result (Kim, wherein the training sets include color images scanned by five different devices/cameras at the different conditions; see fig.5, par.1 and par.2 on page 7).
Regarding claim 14, the combination of Esbech and Kim discloses the method according to claim 12, wherein the artificial intelligence model is a model that learns to classify, if the first scan result is input, the input of the first scan result into the first label (Kim, wherein the classification model SVM was trained by the training sets each of which has an input scan result and its corresponding reference color label; see fig.5, par.1 and par.2 on page 7).
Regarding claim 15, the combination of Esbech and Kim discloses the method according to claim 1, wherein the determining the representative color information comprises: determining recommended color information based on the magnitude of the color similarity (Esbech, “The Euclidian distance between the color data to the selected reference tooth shade value can also be used in determining the certainty measure. If the Euclidian distance is above a threshold value the uncertainty is then evaluated to be too large.”; see para.67); and determining the representative color information based on the user's selection for the recommended color information (Esbech, the system “may involve presenting one or more options to the operator, such as where to derive the tooth shade value and whether to accept a derived tooth shade value.”; see para.154, lines 19-22)
Regarding claim 16, the combination of Esbech and Kim discloses the method according to claim 15, wherein the determining the recommended color information comprises including a reference color whose magnitude of the color similarity exceeds a reference value in the recommended color information (Esbech, “The Euclidian distance between the color data to the selected reference tooth shade value can also be used in determining the certainty measure. If the Euclidian distance is above a threshold value the uncertainty is then evaluated to be too large.”; see para.67).
Regarding claim 17, 18, each of which is an inherent variation of claim 1, thus it is interpreted and rejected for the reasons set forth in the rejection of claim 1.
7. Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Esbech in view of Kim and further in view of Urakabe et al (US20130286174, hereinafter “Urakabe”).
Regarding claim 10, the combination of Esbech and Kim does not disclose, the extracting the first color information set corresponding to the first region further comprises converting the extracted color code into an HSV code. However, in the same field of endeavor, Urakabe teaches, the color component image converting means include HSV color system. See para.498. 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 Urakabe into the teachings of the combination of Esbech and Kim and convert the extracted color code into an HSV code. Suggestion or motivation for doing so would have been to form a panoramic image of the side surface tooth rows for display on a computer monitor as taught by Urakabe, cf., abstract. Therefore, the claim is unpatentable.
Conclusion
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/RUIPING LI/Primary Examiner, Ph.D., Art Unit 2676
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