DETAILED ACTION
Notice of 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 .
Allowable Subject Matter
Claim 8 is 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.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claim(s) 3-4 and 6 is/are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 3, the disclosure does not reasonably convey possession of using machine learned model to perform segmentation and to determine a volume element or at least a central line for each tooth based on results of the segmentation, as recited.
Regarding claim 6, the disclosure does not reasonably convey possession of using machine learning to recognize missing teeth in scout MR images, as recited.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
Claim(s) 10 is/are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Regarding claim 10, “image information” lacks proper antecedent basis.
Regarding claim 10, it is unclear as to whether, “tooth rows being defined based on the identified teeth in the scout MR images”, is being considered to be the “image information”.
Claim Rejections - 35 USC § 103
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 may not be obtained though the invention is not identically disclosed or described as set forth in section 102 of this title, if the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains. Patentability shall not be negatived by the manner in which the invention was made.
Claim(s) 1-3, 5-7, 9, and 11-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Three-dimensional localization of impacted teeth using magnetic resonance imaging” by O. Tymofiyeva et al. Clin Oral Invest. 14:169-179. 2010 (hereinafter as Tymo) in view of “MRI of the teeth” by L.M. Tutton. Brit Journal Rad. 75, 2002, 552-562 (hereinafter as Tutton) in view of “Automated Dental Arch Detection Using Computed Tomography Images” by T. Chanwimaluang et al. IEEE. 737-740 (hereinafter as Chan).
Regarding claims 1 and 11-15, Tymo discloses a method, system, apparatus, controller, and magnetic resonance imaging system for generating dental images from magnetic resonance imaging (MRI) data, comprising: acquiring a number of diagnostic MR images of defined regions, generating a panoramic image based on the number of diagnostic MR images and defined regions, and outputting the panoramic image (Fig. 3: “3D MRI data set and a panoramic curved cut view”). Tymo does not explicitly disclose acquiring scout magnetic resonance (MR) image of a head including a number of teeth of an upper or lower jaw, identifying the teeth in the scout MR images and determining positions of the identified teeth, or that diagnostic images have higher resolution than scout images. However, Tutton teaches performing scout MR imaging of a jaw and identifying teeth in MR images, wherein a scout image would have a lower resolution than a non-scout image (Figs. 1-6). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the scout imaging and identification of Tutton to the imaging of Tymo, as to provide efficient and robust MR acquisition of a subject’s teeth. Neither Tymo nor Tutton explicitly disclose that the identifying is automated. However, Chan teaches automated detection of a dental arch in a tomographic medical image (Abstract). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the automated identification of Chan to the images of Tymo and Tutton, as to provide robust and automated segmentation of a dental arch.
Regarding claim 2, Tymo does not explicitly disclose that identifying the teeth comprises identifying in the scout MR images, molars, canine, front teeth, positions of missing teeth, and tooth gaps corresponding to the missing teeth of the upper and/or lower jaw by determining a curved dental arch based on contrast differences between different bone types and interpolating positions of missing teeth on the dental arch based on identified teeth and/or contrast differences. However, Chan teaches automated detection of a dental arch in a tomographic medical image (Abstract). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the automated identification of Chan to the images of Tymo and Tutton, as to provide robust and automated segmentation of a dental arch.
Regarding claim 3, Tymo does not explicitly disclose that determining landmarks for a plurality of teeth, wherein for a tooth, at least a point on a predefined position at a crown and a number of points at an end of at least one root of the tooth are identified, the landmarks including information about the position of the points and/or lines through the points. However, Tutton teaches identifiying and analyzing the features of both crowns and roots which include root apices and pulp chamber of crowns (Figs. 3 and 4). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the landmarks of Tutton to the image data of Tymo, as to provide a robust description of imaged teeth.
Regarding claim 5, Tymo discloses a panoramic image in an inline display (Fig. 3) but does not explicitly disclose that the inline curved plane reformatting is based on landmarks and/or results from segmentation. However, Chan teaches that a panoramic image is generated based upon segmentation results (Fig. 6b). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the segmentation of Chan to the panoramic generation of Tymo, as to provide robust and accurate generation of based upon an anatomical segmentation.
Regarding claim 6, neither Tymo, Tutton, nor Chan explicitly disclose that the identification of teeth and the determination of their position is performed using a deep machine learning algorithm trained on scout MR images with labelled teeth, the algorithm also being trained to recognize missing teeth by labelled tooth gaps in the scout MR images. the identification of teeth and the determination of their position is performed using a deep machine learning algorithm trained on scout MR images with labelled teeth, the algorithm also being trained to recognize missing teeth by labelled tooth gaps in the scout MR images. However, the Applicant has admitted in regards to performing image segmentation, that “The general architecture and training routines for such models are known in the art”. Thus it would have been obvious to one of ordinary skill in the art to use such known methods for performing the claimed identification and determination.
Regarding claim 7, Tymo does not explicitly disclose that the identification of teeth is performed in a plane at roots of the teeth and/or in a plane at crowns of the teeth. However, Tutton teaches imaging teeth in a plane which includes both the root and the crown (Fig. 2). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the imaging plane of Tutton to the imaging of Tymo, as to provide a robust view of critical features of teeth.
Regarding claim 9, neither Tymo nor Tutton explicitly disclose a region is a bounding box marking a region of interest for the acquisition of the number of diagnostic MR images and which is used to plan slices for subsequent planar 2D scans, and a region is divided into quadrants and the output comprises an annotation of the quadrants and/or addition of individual teeth, the region being selected by a user. However, Chan teaches segmenting and isolating a region of interest for further inspection (Figs 3 and 4). Thus, it would have been obvious to one of ordinary skill in the art before the effective filing date of the present invention to apply the isolation of Chan to the images of Tymo and Tutton, as to provide robust identification of an imaged object.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“Accuracy and efficiency of automatic tooth segmentation in digital dental models using deep learning” by J. Im. Et al. Scientific Reports. 9429, 08 June 2002.
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/JASON M IP/Primary Examiner, Art Unit 3793