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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/14/26 has been entered.
A new rejection is applied based on amendments in the submission.
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) in further view of Arnone et al (US 2023/0113449).
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.
While Kim and Juneja fail to specifically teach the added limitation of the “continuous output value is indicative of where the at least one occlusion images falls within the spectrum of possible occlusions conditions”, Arnone does (see figures 2 and 3) where possible occlusion conditions are presented in a chart form with possible treatments. Thus having a spectrum. Since all three systems are trainable occlusion classification systems, it would have been obvious to one of ordinary skill to include the classification details of Arnone into Kim and Juneja to have a more robust classification network with more treatments suggested.
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, Juneja and Arnone 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, Juneja and Arnone 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.
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
