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 .
Claim Objections
Claim 14 objected to because of the following informalities: In claim 14, line 2, “ore” should be changed to “or”. Appropriate correction is required.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claim(s) 1, 3, 11-15 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Hillen (US 2019/0313963).
As to claim 1, Hillen discloses a computer-implemented method of generating a training data set for a machine learning model, the method comprising:
receiving extra-oral image data of a plurality of candidate patients provided by an extra-oral image device (para. 0023, e.g., imaging machine, 102, e.g. an x-ray machine, which emits x-ray beams, 104, to an x-ray sensor, 106 (e.g., an intra-oral sensor, an extra-oral sensor, etc.) for taking radiographic images of the jaw and teeth of a patient);
determining one or more periodontal structures for each of the plurality of candidate patients based on the received extra-oral image data (para. 0023, e.g. tissues, gum and bone structures) ;
receiving intra-oral scan data of the plurality of candidate patients provided by a handheld intra-oral scanner (para. 0023, 0030, e.g., an intra-oral sensor, real-world images of dental intraoral cameras (e.g., images captured by individuals in real-world conditions that include one or more colored pictures of the teeth and gum inside the patient's mouth)) and
generating a training data set by combining determined extra-oral one or more periodontal structures with the intra-oral scan data for each of the plurality of candidate patients for aligning the one or more periodontal structures to the intra-oral scan data (para. 0030, 0031).
As to claim 3, Hillen discloses a computer implemented method according to claim 1, wherein the determining of the one or more periodontal structures for each of the plurality of candidate patients comprises
identifying in the extra-oral image data a plurality of teeth (para. 0023. 0030, 0033);
determining one or more reference sites per identified tooth of the extra-oral image data (para. 0023. 0030, 0033); and
determining the one or more periodontal structures at each of the one or more reference sites (para. 0023. 0030, 0033).
As to claim 11, Hillen discloses a computer-implemented method for providing a diagnostic data set of a patient comprising receiving the intra-oral scan data of the patient (para.0030;
inputting to the trained machine learning model according to claim 1, the intra-oral scan data (para. 0030, 0033); and
receiving from the trained machine learning model a diagnostic dataset of the patient. (para. 0030, 0033. 0039)
As to claim 12, Hillen discloses a computer implemented method according to claim 11, wherein the outputting of the diagnostic data set of the patient includes one or more periodontal structures determined by the processing of the intra-oral scan data of the patient based on the training data set (para. 0030, 0023, 0039).
As to claim 13, Hillen discloses a computer implemented method according to claim 11, wherein the diagnostic data comprises a prediction that a periodontal structure is present on one or more teeth in the intra-oral scan data and at what location that prediction is present (para. 0023, 0030, 0033, 0034).
As to claim 14, Hillen discloses a computer implemented method according to claim 1, wherein the one or more periodontal structures are presented on a graphical user interface including a 2D model or a 3D model of a mouth anatomy determined by the diagnostic data on which the one or more periodontal structures are visualized (para. 0032, 0039).
As to claim 15, Hillen discloses a computer implemented method according to claim 14, wherein the one or more periodontal structures are visualized at the one or more reference sites when a value of the one or more periodontal structures fulfills a visualization criterium (para. 0032, 0039).
Allowable Subject Matter
Claims 2, 4-10 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter: The prior art discloses the claim limitations discussed above, but fails to disclose the combined features required by each of dependent claims 2, 5, 8.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
INGLESE et al. disclose a method and an apparatus for training automatic tooth charting systems, for example training automatic tooth charting systems based on artificial intelligence such as neural network based tooth charting systems.
Claessen et al. relate to automated determination of a canonical pose of a 3D object, such as 3D dental structure, and superimposition of 3D objects using deep learning.
Elbaz et al. disclose a method that includes receiving intraoral scan data of an intraoral cavity of a patient; processing the intraoral scan data to determine, for each dental condition of a plurality of dental conditions, whether the dental condition is detected for the patient and a severity of the dental condition; and presenting indications of the plurality of dental conditions together in a graphical user interface (GUI), wherein the indications show, for each dental condition of the plurality of dental conditions, whether the dental condition was detected for the patient and the severity of the dental condition.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHUOC TRAN whose telephone number is (571)272-7399. The examiner can normally be reached 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vu Le can be reached at 571-272-7332. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/PHUOC TRAN/Primary Examiner, Art Unit 2668
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