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 .
Response to Amendment
Applicant's amendments and arguments filed 1/7/26 have been fully considered but they are not persuasive.
Applicant’s amendments appear to attempt to define over the occlusion class of Kim et al and Juneja et al by requiring a single continuous occlusion class. The examiner is not sure exactly how a single value is continuous, but this amendment and arguments do not appear to overcome the prior art rejection of record. Both references use the terms of Class I, II and II which are whole number. If applicant is attempting to define the output as a single number, than it appears obvious to one of ordinary skill that selection of one of the classes would meet this limitation. While the examiner noted in the non-final rejection “While Kim et al. fail to explicitly say that the range is continuous”, such explicit teachings are not needed for anticipation nor obviousness. Even if the references teach an average or some non-whole number that does not diminish that the final selection of one of the classes is a single value.
Claim Rejections - 35 USC § 103
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 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) 1-8, 11, 12, 14 and 18-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kim et al. (Malocclusion Classification … supplied by applicant in IDS dated 4/29/25) in view of Juneja et al. (OCLU-NET for occlusal Classification … supplied in IDS dated 4/29/25).
Regarding claims 1, 20 and 21, Kim et al. teach a method for determining a single continuous occlusion class indicator corresponding to occlusion image, the method comprising:
acquiring the at least one occlusion image of an occlusion of a human subject by an image capture device (sets of three images are obtained of orthodontic views page 1294 second full paragraph);
applying at least one computer-implemented occlusion classification neural network (deep learning method) to the at least one occlusion image to determine the at least one occlusion class indicator of the occlusion of the human subject, (page 1294 second full paragraph notes the classes and datasets) the at least one occlusion classification neural network being trained for classification using at least one occlusion training dataset, each given at least one occlusion training dataset including a plurality of occlusion training examples being pre-classified into one of at least:
a first occlusion class, being attributed a first numerical value for the given occlusion type training dataset (Class I normal);
a second occlusion class, being attributed a second numerical value for the given occlusion type training dataset (Class II overbite);
a third occlusion class, being attributed a third numerical value for the given occlusion type training dataset (Class II underbite), wherein the first numerical value is between the second numerical value for the given occlusion type training dataset and the third numerical value for the given occlusion type training dataset (note normal bite is between overbite and underbite);
each occlusion training example comprising:
a respective training occlusion image, being input data; and its respective numerical value, being output data;
wherein the at least one occlusion class indicator of the occlusion of the human subject determined by the at least one computer-implemented occlusion classification neural network is a numerical output value within a range of values having the second numerical value as a first bound and the third numerical value as a second bound (note range of overbite to underbite).
While Kim et al. fail to explicitly say that the range is continuous, Juneja et al show more specifically that the range between class is based on 3D point distributions and linear and nonlinear distances. All of the equations shown in Juneja et al are continuous. Therefore, it would have been obvious to one of ordinary skill in the art at the time of filings to use continuous ranges in the classification of jaw bite classes since Juneja shows that classification is based on distances. Further, it would be well understood by one of ordinary skill that the difference in these orthodontic classifications deeply depends on the center bite distance or depth (page 3 right column first partial paragraph). Further, selection of a single class is what the inventions of Kim and Juneja are purposed for.
Regarding claim 2, it would have been obvious at the time of filing to use a camera found in a mobile device running a mobile application to allow for the training methods of Kim and Juneja since it would provide an equivalent imaging means in a portable environment.
Regarding claims 3-8, Juneja discloses the same Classes and images on page 2, second full paragraph on the left column. Further Kim also notes that each training set has three images (left right and center i.e. three different views) and there are three classes. Selection of one of the classes is what both Kim and Juneja are purposed for.
Regarding claims 11, 12 and 14 Juneja teaches a computer-implemented occlusion classification neural network comprises at least one radial basis function neural network (see page 2, Section 2 second paragraph) shows a radial basis function is known to be used for orthodontic classification.
Regarding claims 18 and 19, Kim et al teaches diagnosing an orthodontic malocclusion (page 1297 section 3B).
Allowable Subject Matter
Claims 9, 10, 13, 16 and 17 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.
Conclusion
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER S KELLEY whose telephone number is (571)272-7331. The examiner can normally be reached Mon-Fri 6:30 to 4 pm alternate Fridays off.
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/CHRISTOPHER S KELLEY/ Supervisory Patent Examiner, Art Unit 2482