DETAILED ACTION
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 .
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1 - 3, 6 - 7, 10 - 12, 15 - 16, 19 - 21, 24 - 25, 31 - 33, and 35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1 - 19 of U.S. Patent No. 11,553,874. Although the claims at issue are not identical, they are not patentably distinct from each other because both sets of claims are directed to methods and systems for analyzing dental information using machine learning and producing a representation based on confidence scores for the presence of a detectable feature. Specifically:
Claims 1, 10, 19, and 33 are suggested by reference claim 1, 8, and/or 14. The reference claims are not specific to the training images comprising a pristine image and a distorted image. In addition, the reference claims are not specific to comparing data using a workflow where data is not visualized, the data including at least one of a position or type of feature associated with the at least one detectable feature to data collected over a period of time to identify progress in the patient. However, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the reference invention to have the training images comprise a pristine image and a distorted image, in order to effectively train the neural network to recognize the detectable feature in images of a variety of signal to noise ratios, such as images that include some degree of distortion. It also would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the reference invention to include comparing data using a workflow where data is not visualized, the data including at least one of a position or type of feature associated with the at least one detectable feature to data collected over a period of time to identify progress in the patient, in order to help a user understand the patient’s progress.
Claims 2, 11, and 20 are suggested by reference claim 2, 9, and/or 15.
Claims 3, 12, and 21 are suggested by reference claim 3, 10, and/or 16.
Claims 6, 15, and 24 are suggested by reference claim 4, 11, and/or 17.
Claims 7, 16, and 25 are suggested by reference claim 5, 12, and/or 18.
Regarding claim 31, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the reference invention to have the adjusting comprise adjusting a representation of distance, in order to facilitate normalizing the data properly for analysis.
Regarding claim 32, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the reference invention to have the anatomical structure characteristics comprise at least bone density or bone dimensions, in order to facilitate analyzing the dental health of the patient using relevant information.
Regarding claim 35, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the reference invention to have the image source information de-identify the patient, in order to anonymize the data to protect patients’ sensitive information.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 - 3, 6 - 7, 10 - 12, 15 - 16, 19 - 21, 24 - 25, 31 - 33, and 35 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 10, and 19 are indefinite for the following reasons:
There is unclear antecedent basis for the word “data” in the recitation “comparing data using a workflow” in the second to last paragraph. It is unclear if these are the previously recited data (i.e., “data representing one or more images of dental information”), or some other data.
There is unclear antecedent basis for the word “data” in the recitation “data is not visualized” in the second to last paragraph. It is unclear which data are being referred to, and consequently, which data that the claims attempt to preclude visualization of.
There is unclear antecedent basis for the word “data” in the recitation “data collected over a period of time” in the second to last paragraph. It is unclear which data are being referred to, and consequently, which data are being compared to which other data.
For the purposes of examination, any comparison of data that include “a position or type of feature associated with the at least one detectable feature”, wherein some data are not visualized, will be interpreted as meeting the claim.
Examiner note: dependent claims that are rejected under 112(b) are indefinite by virtue of dependency.
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 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.
Claims 1 - 2, 6 - 7, 10 - 11, 15 - 16, 19 - 20, 24 - 25, 31 - 33 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Patel (US 2018/0325484, of record) in view of Azernikov et al. (US 2018/0028294, of record), Zhou et al. (US 2019/0205606, of record) and Elbaz et al. (US 20180028065, of record).
Regarding claims 1, 10, and 19, Patel shows a system comprising a processor and a non-transitory memory storing instructions (computing device 100, [0045]-[0051] and fig. 1) that cause the processor to implement a method comprising:
receiving data representing an image of dental information associated with a patient, the image containing unidentified image features of the patient (image for dental diagnosis, abstract; processing an image, [0052] and figs. 2 - 4);
adjusting the data into a predefined format by adjusting visual parameters associated with the image (standardizing all images, [0053] and step 203 of fig. 2); and
using a machine learning system (machine learning, [0032], [0034], [0070]) comprising a neural network (artificial neural network, [0078]) to determine a confidence score (“classification… generates as an output a set of numbers, where each output corresponds to the likelihood of a particular classification …,” [0078]. Examiner maps each output/likelihood value to the claimed “confidence score”) for the presence of a detectable feature (“four (4) different classes … (1) Class 1—No Abnormality/Pathogenesis: … (2) Class 2—Apical Periodontitis …,” [0039] - [0042]),
the confidence score corresponding to a portion of the image (normal/disease regions, bone loss regions, and accessory canal regions, [0035]);
the detectable feature representing a disease or anatomical structure characteristics (“four (4) different classes,” [0039] - [0042]);
the machine learning system being trained with dental imagery comprising training images (machine learning algorithm to train the system to diagnose different cases, [0032]; training the system, [0034]; training algorithm, Table 2) and image source information (implicit - the image data necessarily contain information that somehow relates to the image source); and
generating a report comprising the detectable feature (“information specifying the medical and/or dental diagnosis … stored in a data store or communicated over a network”, [0004]; communicating or storing results, [0077] and step 1612 of fig. 16).
Patel fails to show producing a representation of the confidence score, the representation comprising information about a presence or absence of the detectable feature, and a tooth number associated with the detectable feature, if present, within the image. In addition, Patel is not specific to the training images comprising a pristine image and a distorted image. Further, Patel is silent as to comparing data including a position or type of feature associated with the detectable feature to data collected over a period of time to identify progress in the patient, using a workflow where data is not visualized.
Azernikov discloses dental computer-aided design automation using deep learning. Azernikov teaches producing a representation of a confidence score (“probability of each one of a set of dental features being present in a portion of a patient' dentition,” [0105] - [0106]), the representation comprising information about a presence or absence of a detectable feature, and a tooth number associated with the detectable feature, if present, within an image (“… display the recognized dental information … identify and label … the number of a tooth … train deep neural networks to detect various dental features …,” [0057]).
Zhou discloses artificial intelligence based medical image segmentation. Zhou teaches training images comprising a pristine image and a distorted image ([0040]; “… training samples … generated from the real medical image training samples … different imaging characteristics (e.g., noise levels, resolution, etc.) … machine learning … trained based on the training data (real and synthetic) …,” [0044]; “trained based on medical image training samples and synthetic training samples generated at a plurality [of] noise levels from the medical image training samples”, claim 9).1
Elbaz discloses dental diagnostics. Elbaz teaches comparing data including a position or type of feature associated with the detectable feature to data collected over a period of time to identify progress in the patient (“… follow the teeth over time… tracking dentin, caries, etc., and general dental health …”, [0247]), using a workflow where data is not visualized (implicit - at least some unspecified data are not visualized).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified Patel’s invention to include producing a representation of the confidence score, the representation comprising information about a presence or absence of the detectable feature, and a tooth number associated with the detectable feature, if present, within the image, as taught by Azernikov, in order to communicate the results of the classification process to a clinician in a manner that helps the clinician understand which tooth or teeth may need treatment, as is understood in the art.
It also would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Patel and Azernikov to have the training images comprise a pristine image and a distorted image, as taught by Zhou, in order to use training data having different imaging characteristics, as suggested by Zhou ([0040]; [0044]), to thereby improve robustness of the detection of the detectable feature.
In addition, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Patel, Azernikov, and Zhou to include comparing data including a position or type of feature associated with the detectable feature to data collected over a period of time to identify progress in the patient, using a workflow where data is not visualized, as taught by Elbaz, in order to provide an estimate of severity of dental health concerns, as suggested by Elbaz ([0248]), especially since tracking health over time is notoriously well-understood and conventional in the art of health management.
Regarding claims 2, 11, and 20, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Patel further shows transferring the data to one or more networked computing devices (network based system, [0047] and fig. 14; [0049]; [0066]; [0077]). The transferred data are at least capable of being used for statistical analysis, and therefore meet the claim in the absence of positive recitation of a step of statistical analysis (claim 2) or operations of statistical analysis (claims 11 and 20).
Regarding claims 6, 15, and 24, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Patel further shows that the detectable feature includes a radiolucent lesion (“image with boxes overlaid thereon showing radiolucent regions,” [0069] and fig. 13; signs of osseous destruction such as a radiolucency, [0074]; radiolucency, [0078]).
Regarding claims 7, 16, and 25, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Further, in the combined invention of the prior art, the produced representation includes a graphical representation that is presentable on a user interface of the computing device (Azernikov: “… display the recognized dental information … identify and label … the number of a tooth,” [0057]).
Regarding claim 31, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Patel further shows that the adjusting adjusts a representation of distance (standardization of size, [0053] and step 203 of fig. 2).
Regarding claim 32, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Patel further shows the anatomical structure characteristics comprise bone density or bone dimensions (automatic identification of bone loss and identification of cracked tooth syndrome, [0031]; [0035]; [0044]; [0055]; [0057] - [0058]).
Regarding claim 33, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Further, as the claim only requires the “one or more prior procedures” in the alternative, the prior art need not teach the limitation that the “the one or more prior procedures performed upon the patient comprises one or more procedures performed on one or more teeth” in order to meet the claim. The prior art meets the claim for at least this reason.
Regarding claim 35, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Patel further shows the image source information de-identifies the patient (“… complying with at least the Health Insurance Portability and Accountability Act (“HIPAA”) confidentiality requirements …”, [0065]).
Claims 3, 12, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Patel, Azernikov, Zhou, and Elbaz as applied to claims 1, 10, and 19 above, and further in view of Srivastava et al. (“Detection of Tooth Caries in Bitewing Radiographs Using Deep Learning.” NIPS 2017 workshop on Machine Learning for Health (NIPS 2017 ML4H). arXiv preprint arXiv:1711.07312. https://arxiv.org/abs/1711.07312, of record).
Regarding claims 3, 12, and 21, the combined invention of Patel, Azernikov, Zhou, and Elbaz discloses the claimed invention substantially as noted above. Patel further shows that the machine learning system employs a neural network (artificial neural network, [0078]).
Patel is silent as to whether or not the neural network is a convolution neural network.
Srivastava discloses detection of tooth caries in bitewing radiographs using deep learning. Srivastava teaches a neural network that is a convolution neural network (deep fully convolutional neural network, abstract).
It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the combined invention of Patel, Azernikov, Zhou, and Elbaz to have the neural network be a convolution neural network, as taught by Srivastava, in order to make it possible to provide an end-to-end solution that detects dental cavities directly from the original radiographs without needing any specialized tailoring of images (e.g., detection in extracted tooth artificially arranged for the process) as discussed by Srivastava (pg. 2, section 2).
Response to Arguments
Applicant's arguments filed 10/28/2025 have been fully considered but they are not persuasive.
Applicant's request on page 9 to hold the double patenting rejections in abeyance is acknowledged. Applicant's comments do not clearly identify supposed error in the rejections. The rejections are deemed proper and are maintained for reasons detailed above.
Applicant argues on page 11 that “the Examiner indicated that the features now
recited in Claim 1 are likely to overcome the outstanding rejections”. However, as noted in the interview summary “no specific claim language was discussed/agreed to.” The prior art appears to meet the claims for reasons explained in the art rejections above, as best understood in light of the clarity deficiencies discussed in the 112(b) rejections above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 AMELIE R DAVIS whose telephone number is (571)270-7240. The examiner can normally be reached Monday-Friday, 9:30 - 6:00 PST.
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/AMELIE R DAVIS/Primary Examiner, Art Unit 3798
1 Examiner note: the ‘real medical images’ and ‘synthetic medical images’ are respectively mapped to the instant “pristine” and “distorted” medical images