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. This is in response to the applicant response filed on 09/17/2026. In the applicant’s response, no claim was amended, cancelled, or newly added. Accordingly, claims 1-18 are pending and being examined. Claims 1, 17, and 18 are independent form.
Claim Rejections - 35 USC § 103
3. 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.
4. 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.
5. 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 103 of fig.1 and para.140: “a digital 3D representation of the tooth [selected by the user] is generated from said sub-scans”; wherein each sub-scan includes “a sequence of 2D images”, see par.160, lines 1-5);
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.171: “For a given point P on the digital 3D representation, the color data (
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p
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,
a
p
*
,
b
p
*
) has been determined, e.g. by combining the color information of a series of sub-scans used for generating the digital 3D representation.”);
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 (see para.174, to determine the tooth shade value for the point, calculate the Euclidean distance (i.e., the similarity) between the input color information (
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,
a
p
*
,
b
p
*
) and the known colors (
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b
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,
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=
1
,
2
,
.
.
.
k
) of the reference tooth shade values and selecting the smallest reference/representative shade tab).
As explained above, the mere difference is, Esbech does not disclose “an artificial intelligence model configured to output a color similarity based on an input of the first color information set” but instead uses the Euclidean Distance model to obtain the most similar representative color. However, in the same field of endeavor, Kim teaches this. Specifically, Kim teaches, using a trained SVM (i.e., an artificial intelligence) model to output a most similar representative color to one of the natural tooth colors based on 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.
6. 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.
Response to Arguments
7. Applicant’s arguments, filed on 09/17/2026, have been fully considered but they are not persuasive.
On pages 4-5, the applicant submits:
“However, it is respectfully submitted that the cited references, i.e., Esbech, Kim and
Uraka be, whether taken alone or in combination, fail to disclose or suggest the above Features A to C of Claim 1, for at least the reason that the manners of color determination of the cited
references are fundamentally different from that of the claimed invention.”
However, the applicant does not point out which element(s) is/are missed by the prior arts but recited by the claims. The examiner respectfully points out that claim 1 only recites a method, the method comprises the following elements:
[1] 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;
[2] 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;
[3] using an artificial intelligence model configured to output a color similarity based on an input of the first color information set; and
[4] determining representative color information of a first unit object corresponding to the first point based on a magnitude of the color similarity.
As explained in the rejections of the claims, the combination of Esbech and Kim discloses all the elements. Specifically, Esbech discloses:
For element [1], see 103 of fig.1 and para.140: “a digital 3D representation of the tooth [selected by the user] is generated from said sub-scans”; see par.160, lines 1-5: wherein each sub-scan includes “a sequence of 2D images”.
For element [2], see para.171: “[f]or a given point P [of the selected region of the selected tooth] on the digital 3D representation, the color data (
L
p
*
,
a
p
*
,
b
p
*
) has been determined, e.g. by combining the color information of a series of sub-scans used for generating the digital 3D representation”. In other words, the method in Esbech generates a color data (
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p
*
,
a
p
*
,
b
p
*
) corresponding to each point p of the selected region of the selected tooth, and thus generates a color information set, (
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,
a
p
*
,
b
p
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), p=1, 2, ..., k, corresponding to the set of the k points included in the selected region of the selected tooth.
For elements [3] and [4], see para.174, to determine the tooth shade value for the point, calculate the Euclidean distance (i.e., the similarity) between the input color information (
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p
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,
a
p
*
,
b
p
*
) and the known colors (
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,
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=
1
,
2
,
.
.
.
k
) of the reference tooth shade values and selecting the most similar representative color corresponding to the input color information.
As such, Esbech discloses the claimed invention except for “an artificial intelligence model” configured to output a color similarity. However, in the same field of endeavor, Kim teaches this. Specifically, Kim teaches, using a trained SVM (i.e., an artificial intelligence) model to output a most similar representative color to one of the natural tooth colors based on 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.
8. In view of the above reasons, examiner maintains rejections.
Conclusion
9. THIS ACTION IS MADE FINAL. 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 mailing date of this final action.
10. 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
