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 .
Response to Amendment
Claims 1-24 were previously pending and subject to non-final action filed 12/17/2025. In the response filed on 03/17/2026, claim 1, 15, 19, and 22 were amended. Therefore, claims 1-24 are currently pending and subject to the final action below.
Response to Arguments
Applicant's arguments filed 03/17/2026, with respect to claims 1-24 under U.S.C. 101 have been fully considered but they are not persuasive.
Applicant’s argument: Claims 1-24 were rejected under 35 U.S.C. § 101 as allegedly being directed to an abstract idea without significantly more.
The Office Action, on page 2, has alleged that deriving a plurality of parameters from images and generating a restorative decision recommendation covers concepts performed in the human mind. However, claims 1, 15, 19, and 22, as amended, recite parameters derived from image data corresponding to one or more imaging modalities (such as intraoral scans, radiographs, or CBCT scans) and applying a decision model with clinician-tunable parameters that define conditions for determining which type of restorative decision recommendation is generated. These steps cannot practically be performed in the human mind. This is consistent with Example 39 (Facial Detection) from the USPTO Subject Matter Eligibility Examples, where claims directed to training neural networks and applying transformations to images were found to not recite a judicial exception because "the steps are not practically performed in the human mind." See USPTO, Subject Matter Eligibility Examples: Abstract Ideas, Example 39 (Jan. 2019).
Even if the claims were found to recite an abstract idea, the claims integrate any such idea into a practical application by providing a specific technological improvement in dental diagnostics, namely, generating restorative decision recommendations (e.g., direct restoration vs. indirect restoration) to inform treatment decisions based on parameters derived from multiple imaging modalities. This is consistent with Example 3 (Digital Image Processing), where the claims were found to go "beyond the mere concept of simply retrieving and combining data using a computer" and show an improvement in the technology of digital image processing. See USPTO, Subject Matter Eligibility Examples: Abstract Ideas, Example 3 (Dec. 2016).
Further, claim 15 recites presenting in a GUI a 2D or 3D image of the intraoral cavity and an indication of the tooth having the associated dental condition, along with a restorative decision recommendation to inform a treatment decision. Similarly, claim 15 provides a specific improvement by presenting the restorative decision recommendation to inform a treatment decision, along with an indication of the tooth having the associated dental condition.
Examiner Response: After careful consideration and review of applicant’s argument. The claim amendments continue to recite an abstract idea. Furthermore, there appears to be no transformation as defined in example 39.
Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claims 1, 15, 19 and 22 are directed to a method, and system, which is directed to a process, one of the statutory categories.
Step 2A prong 1: Does the claim recite a judicial exception? Yes, claims 1, 15, 19 and 22 recites similar limitations of: “deriving a plurality of parameters from images, generating a restorative decision recommendation, and determining which type of restorative decision recommendation” because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application? No, The claim recites additional element of “a memory, a processing device, non-transitory computer-readable storage medium, receiving image data of an intraoral cavity of a patient, applying a decision model, a graphical user interface (GUI) and applying a trained machine learning mode, wherein the decision model comprises one or more clinician-tunable parameters that define conditions”.
With regard to the “applying a decision model, a trained machine learning” with memory, processing device for receive image data to generate an output within a graphical user interface (GUI) is akin to programming a computer to perform a function “apply it” limitation in accordance with MPEP 2106.05(f). The use of “a decision model”, “a trained machine learning model” is similar to an “off the shelf” component.
The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application. Thus, the claims is directed to an abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception? No, As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a memory, a processing device, non-transitory computer-readable storage medium, receiving image data of an intraoral cavity of a patient, applying a decision model, a graphical user interface (GUI) and applying a trained machine learning model” only amount to “apply it” consideration (MPEP 2106.5 (f)). With regard to the “trained” machine learning model and “decision model” is being trained with specific data is considered a generic link to a particular “field of use” in accordance with MPEP 2106.05(h).
The receiving of image data amounts to insignificant extra-solution activity as well as WURC activities to condition the data for input into the neural network. Similar to “receiving or transmitting data over the network, e.g., using the Internet to gather data, Symantec, 832 F.3d at 1362 (utilizing intermediary computer to forward information)” See MPEP 2106.05 (d). These limitations, taken alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
The dependent claims the additional limitations (in claims 2-14, 16-18, 20-21, and 23-24) also constitute concepts to “apply it” which fall within the “Mental Processes” and groupings of abstract ideas.
This judicial exception is not integrated into a practical application and amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above.
Applicant’s arguments, see pages 8-9, filed on 03/17/2026 with respect to claim(s) 1-2, 4-6, 8-13, 15-24 under 35 U.S.C. 102 have been considered but are moot because the arguments do not apply to the new combination of references being used in the current rejection.
Applicant’s arguments, see pages 9-10, filed on 03/17/2026 with respect to claim(s) 3, 7 and 14 under 35 U.S.C. 103 have been considered but are moot because the arguments do not apply to the new combination of references being used in the current rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 Analysis: Claims 1, 15, 19 and 22 are directed to a method, and system, which is directed to a process, one of the statutory categories.
Step 2A prong 1: Does the claim recite a judicial exception? Yes, claims 1, 15, 19 and 22 recites similar limitations of: “deriving a plurality of parameters from images, generating a restorative decision recommendation, and determining which type of restorative decision recommendation” because they cover concepts performed in the human mind, including observation, evaluation, judgment, and opinion.
Step 2A prong 2: Does the claim recite additional elements? Do those additional elements, individually and in combination, integrate the judicial exception into a practical application? No, The claim recites additional element of “a memory, a processing device, non-transitory computer-readable storage medium, receiving image data of an intraoral cavity of a patient, applying a decision model, a graphical user interface (GUI) and applying a trained machine learning mode, wherein the decision model comprises one or more clinician-tunable parameters that define conditions”.
With regard to the “applying a decision model, a trained machine learning” with memory, processing device for receive image data to generate an output within a graphical user interface (GUI) is akin to programming a computer to perform a function “apply it” limitation in accordance with MPEP 2106.05(f). The use of “a decision model”, “a trained machine learning model” is similar to an “off the shelf” component.
The additional elements, alone and in combination, fail to integrate the abstract idea into a practical application. Thus, the claims is directed to an abstract idea.
Step 2B: Do the additional elements, considered individually and in combination, amount to significantly more than the judicial exception? No, As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a memory, a processing device, non-transitory computer-readable storage medium, receiving image data of an intraoral cavity of a patient, applying a decision model, a graphical user interface (GUI) and applying a trained machine learning model” only amount to “apply it” consideration (MPEP 2106.5 (f)). With regard to the “trained” machine learning model and “decision model” is being trained with specific data is considered a generic link to a particular “field of use” in accordance with MPEP 2106.05(h).
The receiving of image data amounts to insignificant extra-solution activity as well as WURC activities to condition the data for input into the neural network. Similar to “receiving or transmitting data over the network, e.g., using the Internet to gather data, Symantec, 832 F.3d at 1362 (utilizing intermediary computer to forward information)” See MPEP 2106.05 (d). These limitations, taken alone or in combination, fail to provide an inventive concept. Thus, the claim is not patent eligible.
The dependent claims the additional limitations (in claims 2-14, 16-18, 20-21, and 23-24) also constitute concepts to “apply it” which fall within the “Mental Processes” and groupings of abstract ideas.
This judicial exception is not integrated into a practical application and amount to no more than adding insignificant extra-solution activity/specifications related to data gathering, data input, or data transmittal. These additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above.
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 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) ) 1-2, 4-6, 8-13, 15-24 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman (US 20180168781 A1, Pub Date: Jun. 21, 2018) in view of Mason (US 20160135925 A1, Pub Date: May 19, 2016)
Regarding independent claim 1, Kopelman teaches: A method of providing restorative decision support for a dental patient, the method comprising: (Kopelman − [0054] FIG. 1A illustrates one embodiment of an AR system 100 for providing augmented reality enhancements to a dental practitioner. [0063] By way of non-limiting example, dental procedures may be broadly divided into prosthodontic (restorative) and orthodontic procedures; A prosthesis may include any restoration such as implants, crowns, veneers, inlays, onlays, and bridges, for example, and any other artificial partial or complete denture.)
receiving image data of an intraoral cavity of a patient, (Kopelman − [abstract] The computing device receives the intraoral images from the intraoral scanner [0043] stream of images of a patient's oral cavity and dental arch and determine an area of interest present in the image data. )
the image data corresponding to one or more imaging modalities; (Kopelman − [0101] current X-rays, 2D intraoral images, 3D intraoral images or the like. (CT) scan)
deriving a plurality of parameters from the image data; (Kopelman − [0092] The dental condition identifiers 174 may operate on the original unprocessed image data 162 or may operate on processed image data that has been processed by the dental arch/oral cavity identifier 166 and/or the dental arch segmenter 172. For example, one dental condition identifier 174 may be a broken tooth identifier, which may separately perform broken tooth identification for each tooth identified by dental arch segmenter 172. Examples of dental condition identifiers 174 include a broken tooth identifier, a plaque identifier, a tooth wear identifier, an oral cancer identifier, a gum discoloration identifier, a tooth discoloration identifier, a malocclusion identifier, a gum recession identifier, a swollen gum identifier, and so on.) condition identifiers are the parameters.
applying a decision model to the plurality of parameters; (Kopelman − [0095-0097] [0095] the dental condition identifiers 174 may apply rules (e.g., including algorithms, models and/or profiles) that compare dentition features (also referred to as dental features) from a received image to reference data 190, which may include a store of dentition features. [0096] the dental condition identifiers 174 may perform analysis of a dentition feature using machine learning algorithms. For example, a dental condition profile 192 may be trained based on reference data 190 to correlate dentition features in the reference data 190 with associated clinical diagnosis of AOIs.) algorithms, models, and/or profiles is decision model, dental condition identifiers are the parameters.
and generating a restorative decision recommendation based on an output of the decision model to inform a treatment decision for the dental patient, (Kopelman − [0046] In some embodiments, the AR system may provide interactive feedback or other updated information to the dental practitioner based on an interaction with the patient. For example, the feedback may be provided during an intra-oral treatment such as a dental procedure. In some embodiments, the AR system may output to a display of the AR system recommended steps to take during an implant procedure, drilling procedure, grinding procedure, etc. For example, the AR system may show where to remove material for an insertion path, potential undercuts of neighboring teeth, placement of a hole for an implant, drilling depth, drilling direction, or the like. Similarly, the AR system may provide an indication of material to remove during interproximal reduction. [0203] FIG. 15A illustrates an example portion 1510 of a view of an AR display showing a visual overlay with an indication 1515 of an amount of tooth to remove in an interproximal region between two teeth.)
Kopelman does not explicitly teach: wherein the decision model comprises one or more clinician-tunable parameters that define conditions for determining which type of restorative decision recommendation is generated.
However, Mason teaches: wherein the decision model comprises one or more clinician-tunable parameters that define conditions for determining which type of restorative decision recommendation is generated. (Mason − Fig. 11A-G [0181] list of potential treatment options or solutions for the conditions 1110, [0193] Optionally, if multiple treatment solutions are available, the interface 1100 can include tools for comparing the characteristics (e.g., cost, duration etc.) and/or outcomes of different treatments to facilitate decision making; thereby facilitating visual comparison of the efficacy of different treatments. As another example, the predicted cost, duration, and/or any other relevant parameters for each treatment solution can be displayed and compared, e.g., in list or table format. Examiner Note: Solutions are the restorative decision recommendation from the model (algorithm).) Examiner Note: “clinician-tunable” parameters is not defined in the specification, one of ordinary skill interprets the term as parameters for treatment (cost, solutions)..
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kopelman and Mason as each invention in the field of dentistry with the use of machine learning processing of image data. Adding the teaching of Mason provide an interface displaying multiple outcomes of different treatment solutions. One of ordinary skill in the art would have been motivated to make these modification to improve predicting and proactively correcting dental or orthodontic conditions.
Regarding dependent claim 2, depends on claim 1, Kopelman teaches: wherein the one or more imaging modalities comprises an imaging modality selected from an intraoral scan, a radiograph, or a cone-beam computed tomography (CBCT) scan. (Kopelman − [0101] Patient data 188 may include past data regarding the patient (e.g., medical records), previous or current scanned images or models of the patient, current or past X-rays, [0198] the update image data may include a CT scan, x-ray image data, or other image data than was received in block 1410. [0202] As a non-limiting set of examples, the treatment or procedures discussed with reference to FIG. 14 may include placing attachments, interproximal reduction, computer tomography (CT) or x-ray scanning) X-rays is a type of radiograph
Regarding dependent claim 4, depends on claim 2, Kopelman teaches: wherein the one or more imaging modalities comprise the radiograph, (Kopelman − [0101] Patient data 188 may include past data regarding the patient (e.g., medical records), previous or current scanned images or models of the patient, current or past X-rays,) X-rays is a type of radiograph
and wherein the image data comprises one or more of a panoramic radiograph, a bitewing radiograph, or a periapical radiograph. (Kopelman − [0080] The scan mode allows the dental practitioner to capture images and/or video (e.g., bite segment)) The bite segment is bitewing. intraoral scan application 109 may register and stitch together intraoral images from the intraoral scanner 180.)
Regarding dependent claim 5, depends on claim 2, Kopelman teaches: wherein the image data corresponds to two or more of the imaging modalities. (Kopelman − [0101] Patient data 188 may include past data regarding the patient (e.g., medical records), previous or current scanned images or models of the patient, current or past X-rays, [0198] the update image data may include a CT scan, x-ray image data, or other image data than was received in block 1410. [0202] As a non-limiting set of examples, the treatment or procedures discussed with reference to FIG. 14 may include placing attachments, interproximal reduction, computer tomography (CT) or x-ray scanning)
Regarding dependent claim 6, depends on claim 1, Kopelman teaches: wherein the plurality of parameters are each selected from a geometric parameter, a volume/area parameter, or a fracture classification parameter. (Kopelman − [0092] For example, one dental condition identifier 174 may be a broken tooth identifier,) broken is a type of dental facture and is fracture classification parameter.
Regarding dependent claim 8, depends on claim 6, Kopelman teaches: wherein the volume/area parameter comprises one or more of a restorative volume proportion, a decay volume proportion, or a restorative surface proportion. (Kopelman − [0065] Area of interest (AOI) areas indicative of tooth decay, [0122] the conveyed information may include classification of an AOI, a size of an AOI. [0124] AOI classification, indicators may identify classifications assigned to intraoral areas of interest.) size being the volume/area of tooth decay
Regarding dependent claim 9, depends on claim 6, Kopelman teaches: wherein the fracture classification parameter comprises information descriptive of a tooth fracture location and a tooth fracture depth. (Kopelman − [0059] For example, the computing device 105 may use the stereoscopic image data to identify a three dimensional location of a tooth in the field of view of the image capture device 160. The stereoscopic image data may be used to provide an estimation of depth for objects (broken tooth) [0092] For example, one dental condition identifier 174 may be a broken tooth identifier, [0122] the conveyed information may include classification of an AOI, a size of an AOI. [0124] AOI classification, indicators may identify classifications assigned to intraoral areas of interest.)
Regarding dependent claim 10, depends on claim 1, Kopelman teaches: wherein the decision model comprises one or more of a decision tree or a neural network. (Kopelman − [0095-0097] [0095] the dental condition identifiers 174 may apply rules (e.g., including algorithms, models and/or profiles) that compare dentition features (also referred to as dental features) from a received image to reference data 190, which may include a store of dentition features. [0096] the dental condition identifiers 174 may perform analysis of a dentition feature using machine learning algorithms. For example, a dental condition profile 192 may be trained based on reference data 190 to correlate dentition features in the reference data 190 with associated clinical diagnosis of AOIs.) algorithms, models, and/or profiles is decision model, dental condition identifiers are the parameters.
Regarding dependent claim 11, depends on claim 1, Kopelman teaches: wherein the restorative decision recommendation comprises one or more of: a direct restoration recommendation or an indirect restoration recommendation, or an indication of a dental condition and a severity level for the dental condition. (Kopelman − [0046] the AR system may output to a display of the AR system recommended steps to take during an implant procedure, drilling procedure, grinding procedure, etc. For example, the AR system may show where to remove material for an insertion path, potential undercuts of neighboring teeth, placement of a hole for an implant, drilling depth, drilling direction, or the like. Similarly, the AR system may provide an indication of material to remove during interproximal reduction. In some embodiments, the AR system may provide feedback regarding placement of an attachment on a tooth. [0092] detect multiple different types of dental conditions [0193] For example, AOI 1235 may indicate a small area of plaque, while AOI 1240 may indicate a large area of plaque. In some embodiments, the indicators may be of different shapes or styles to indicate AOIs of different types or severities.)
Regarding dependent claim 12, depends on claim 11, Kopelman teaches: wherein the dental condition is selected from a group consisting of caries, gum recession, tooth wear, malocclusion, tooth crowding, tooth spacing, plaque, tooth stains, tooth cracks, cervical defects, and chipped or broken teeth. (Kopelman − [0092] The dental condition identifiers 174 may operate on the original unprocessed image data 162 or may operate on processed image data that has been processed by the dental arch/oral cavity identifier 166 and/or the dental arch segmenter 172. For example, one dental condition identifier 174 may be a broken tooth identifier, which may separately perform broken tooth identification for each tooth identified by dental arch segmenter 172. Examples of dental condition identifiers 174 include a broken tooth identifier, a plaque identifier, a tooth wear identifier, an oral cancer identifier, a gum discoloration identifier, a tooth discoloration identifier, a malocclusion identifier, a gum recession identifier, a swollen gum identifier, and so on.) condition identifiers are the parameters.
Regarding dependent claim 13, depends on claim 1, Kopelman teaches: further comprising: presenting the restorative decision recommendation for display in a graphical user interface (GUI). (Kopelman − [0046] the AR system may output to a display of the AR system recommended steps to take during an implant procedure, drilling procedure, grinding procedure, etc. For example, the AR system may show where to remove material for an insertion path, potential undercuts of neighboring teeth, placement of a hole for an implant, drilling depth, drilling direction, or the like. Similarly, the AR system may provide an indication of material to remove during interproximal reduction. In some embodiments, the AR system may provide feedback regarding placement of an attachment on a tooth. [0050] Embodiments of the present invention enable a user to perform operations (such as to control or navigate a user interface’ while still engaged with a patient; [0191] Fig. 12A and 12B The indicators 1225 shown in FIG. 12A highlight specific areas of tooth wear in a color that contrasts with the tooth color. In some embodiments, the AR system may select the color based on the AOI being tooth wear and another color could indicate another type of AOI.)
Regarding independent claim 15, Kopelman teaches: A method comprising:
identifying a tooth having an associated dental condition based on a first set of parameters (Kopelman − [0092] The dental condition identifiers 174 may operate on the original unprocessed image data 162 or may operate on processed image data that has been processed by the dental arch/oral cavity identifier 166 and/or the dental arch segmenter 172. For example, one dental condition identifier 174 may be a broken tooth identifier, which may separately perform broken tooth identification for each tooth identified by dental arch segmenter 172. Examples of dental condition identifiers 174 include a broken tooth identifier, a plaque identifier, a tooth wear identifier, an oral cancer identifier, a gum discoloration identifier, a tooth discoloration identifier, a malocclusion identifier, a gum recession identifier, a swollen gum identifier, and so on.) condition identifiers are the parameters.
derived from current image data of an intraoral cavity of a patient; (Kopelman − [abstract] The computing device receives the intraoral images from the intraoral scanner [0043] stream of images of a patient's oral cavity and dental arch and determine an area of interest present in the image data. [0170] The image data may include a two dimensional image or video data. In some embodiments, the received image data may include a three dimensional image or stereoscopic image data generated from a variety of image capture devices.)
presenting in a graphical user interface (GUI) a 2D or 3D image of the intraoral cavity of the patient (Kopelman − [0050] Embodiments of the present invention enable a user to perform operations (such as to control or navigate a user interface and/or to manipulate intraoral images or a representation generated from intraoral images) while still engaged with a patient; [0177-0178] Fig. 8A and 8B The image data 810 shows a current image of a patient's smile as seen by a dental practitioner through the AR display. The image data 810 in FIG. 8A is shown as a two dimensional image, but may also be used by an AR system as stereoscopic image data, scan data, or other image data.)
and an indication of the tooth having the associated dental condition; (Kopelman − [0191] Fig. 12A and 12B The indicators 1225 shown in FIG. 12A highlight specific areas of tooth wear in a color that contrasts with the tooth color. In some embodiments, the AR system may select the color based on the AOI being tooth wear and another color could indicate another type of AOI.)
and presenting in the GUI a restorative decision recommendation based on an output of a decision model for which the first set of parameters is used as input. (Kopelman − [0191] Fig. 12A and 12B The indicators 1225 shown in FIG. 12A highlight specific areas of tooth wear in a color that contrasts with the tooth color. In some embodiments, the AR system may select the color based on the AOI being tooth wear and another color could indicate another type of AOI. [0203] FIG. 15A illustrates an example portion 1510 of a view of an AR display showing a visual overlay with an indication 1515 of an amount of tooth to remove in an interproximal region between two teeth. The amount of tooth to remove may be determined based on a three dimensional model of the patient's dental arch as well as planned steps in an orthodontic treatment plan. In addition, the AR system may change a color of the overlay or provide an indication when the planned amount of material has been removed from the tooth. For example, FIG. 15B illustrates an example portion 1520 of a view of an AR display with an updated overlay based on the material removed from the tooth.)
Kopelman does not explicitly teach: and one or more clinician-tunable parameters are used as input for determining a type of the restorative decision recommendation.
However, Mason teaches: and one or more clinician-tunable parameters are used as input for determining a type of the restorative decision recommendation. (Mason − Fig. 11A-G [0181] list of potential treatment options or solutions for the conditions 1110, [0193] Optionally, if multiple treatment solutions are available, the interface 1100 can include tools for comparing the characteristics (e.g., cost, duration etc.) and/or outcomes of different treatments to facilitate decision making; thereby facilitating visual comparison of the efficacy of different treatments. As another example, the predicted cost, duration, and/or any other relevant parameters for each treatment solution can be displayed and compared, e.g., in list or table format. Examiner Note: Solutions are the restorative decision recommendation from the model (algorithm).) Examiner Note: “clinician-tunable” parameters is not defined in the specification, one of ordinary skill interprets the term as parameters for treatment (cost, solutions)..
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kopelman and Mason as each invention in the field of dentistry with the use of machine learning processing of image data. Adding the teaching of Mason provide an interface displaying multiple outcomes of different treatment solutions. One of ordinary skill in the art would have been motivated to make these modification to improve predicting and proactively correcting dental or orthodontic conditions.
Regarding dependent claim 16, depends on claim 15, Kopelman teaches: wherein the restorative decision recommendation is presented in the GUI responsive to a user selection of the tooth in the 2D or 3D image, (Kopelman – [0084] The image processing mode allows the dental practitioner to view the scans in detail at various angles by rotating, moving, zooming in or out, etc. of the virtual 3D model. [0191] Fig. 12A and 12B The indicators 1225 shown in FIG. 12A highlight specific areas of tooth wear in a color that contrasts with the tooth color. In some embodiments, the AR system may select the color based on the AOI being tooth wear and another color could indicate another type of AOI. [0203] FIG. 15A illustrates an example portion 1510 of a view of an AR display showing a visual overlay with an indication 1515 of an amount of tooth to remove in an interproximal region between two teeth. The amount of tooth to remove may be determined based on a three dimensional model of the patient's dental arch as well as planned steps in an orthodontic treatment plan.)
and wherein the indication comprises one or more of a label on the tooth, an outline over the tooth, or a color of the tooth. (Kopelman – [0044] For example, a tooth may be highlighted in a different color, circled, or otherwise indicated as having a characteristic in a visual overlay displayed by the AR system. [0169] Processing logic may additionally add to the visual overlay a label for each of the one or more teeth that are visible in the image data, where the label indicated an identity of a tooth (e.g., identifies tooth 5) [0191] Fig. 12A and 12B The indicators 1225 shown in FIG. 12A highlight specific areas of tooth wear in a color that contrasts with the tooth color. In some embodiments, the AR system may select the color based on the AOI being tooth wear and another color could indicate another type of AOI. [0203] In addition, the AR system may change a color of the overlay or provide an indication when the planned amount of material has been removed from the tooth. For example, FIG. 15B illustrates an example portion 1520 of a view of an AR display with an updated overlay based on the material removed from the tooth.)
Regarding dependent claim 17, depends on claim 15, Kopelman teaches: wherein identifying the tooth having the associated dental condition comprises: comparing the first set of parameters to a second set of parameters derived from prior image data of the intraoral cavity captured prior to the current image data; (Kopelman – [0042] For example, the AR system may provide information about a dental arch based on images captured of the patient by the AR system. The AR system may also provide additional information based on a comparison of images captured by the AR system and previous data recorded for the patient. For example, previous images, scans, models, clinical data or other patient history may be compared to the images captured by the AR system, and a result of the comparison may be provided to the dental practitioner as a visual overlay on the real-world scene viewed by the dental practitioner through an AR display of the AR system. [0045] For example, the AR system may compare images or models from a previous visit to current images of the patient's dental arch. The AR system may then determine one or more areas of interest based on the comparison. For example, the AR system may identify changes since a last scan, analysis of wear over time, feedback on orthodontic treatment, or other analysis of changes. The AR system may then mark the changes on a display of the AR system. In some embodiments, the AR system may also superimpose previous patient data on a display. For example, the AR system may show a previous scan or previous dental arch superimposed onto a display.)
and identifying the tooth by determining that a difference between a parameter from the first set of parameters and a parameter from the second set of parameters satisfies a threshold condition. (Kopelman – [0110] [0123] [0151] [0155] [0110] Prior data comparator 180 may compare the determined magnitude of change and/or the determined rate of change of the dental condition to general norms for the dental condition. The general norms may include rate of change thresholds for the dental condition. If the determined rate of change exceeds a rate of change threshold, then prior data comparator 180 may generate a notice or flag for the dental practitioner calling out an abnormal change in the dental condition. [0151] For example, differences such as changes in tooth wear, changes in gum recession, changes in gum color, changes in tooth color, changes in gum swelling, and so on may be identified. In one embodiment, processing logic may identify those changes that are over a threshold value for an amount of change. [0155] At block 415, processing logic determines a previous date associated with the previous image data and a current date associated with current image data. At block 440, processing logic generates a visual overlay including the change in the dental condition and/or an indication that the difference between the target rate of change and the rate of change that was identified exceeds the rate of change threshold.)
Regarding dependent claim 18, depends on claim 17, Kopelman teaches: wherein the current image data corresponds to a first imaging modality, (Kopelman − [0101] Patient data 188 may include past data regarding the patient (e.g., medical records), previous or current scanned images or models of the patient, current or past X-rays, [0202] As a non-limiting set of examples, the treatment or procedures discussed with reference to FIG. 14 may include placing attachments, interproximal reduction, computer tomography (CT) or x-ray scanning) and wherein the prior image data corresponds to a second imaging modality that is different from the first imaging modality. (Kopelman − [0101] [0202] As a non-limiting set of examples, the treatment or procedures discussed with reference to FIG. 14 may include placing attachments, interproximal reduction, computer tomography (CT) or x-ray scanning)
Regarding independent claim 19, Kopelman teaches: A method comprising:
receiving image data corresponding to an intraoral cavity of a patient; (Kopelman − [abstract] The computing device receives the intraoral images from the intraoral scanner [0043] stream of images of a patient's oral cavity and dental arch and determine an area of interest present in the image data. )
applying a trained machine learning model to the image data to derive a plurality of parameters from the image data; (Kopelman − [0095-0097] [0095] the dental condition identifiers 174 may apply rules (e.g., including algorithms, models and/or profiles) that compare dentition features (also referred to as dental features) from a received image to reference data 190, which may include a store of dentition features. [0096] the dental condition identifiers 174 may perform analysis of a dentition feature using machine learning algorithms. For example, a dental condition profile 192 may be trained based on reference data 190 to correlate dentition features in the reference data 190 with associated clinical diagnosis of AOIs.)
and applying a decision model to the plurality of parameters to generate a restorative decision recommendation to inform a treatment decision for the dental patient, (Kopelman – [0095-0097] [0046] In some embodiments, the AR system may provide interactive feedback or other updated information to the dental practitioner based on an interaction with the patient. For example, the feedback may be provided during an intra-oral treatment such as a dental procedure. In some embodiments, the AR system may output to a display of the AR system recommended steps to take during an implant procedure, drilling procedure, grinding procedure, etc. For example, the AR system may show where to remove material for an insertion path, potential undercuts of neighboring teeth, placement of a hole for an implant, drilling depth, drilling direction, or the like. Similarly, the AR system may provide an indication of material to remove during interproximal reduction.)
Kopelman does not explicitly teach: wherein the decision model comprises one or more clinician-tunable parameters that define conditions for determining which type of restorative decision recommendation is generated.
However, Mason teaches: wherein the decision model comprises one or more clinician-tunable parameters that define conditions for determining which type of restorative decision recommendation is generated. (Mason − Fig. 11A-G [0181] list of potential treatment options or solutions for the conditions 1110, [0193] Optionally, if multiple treatment solutions are available, the interface 1100 can include tools for comparing the characteristics (e.g., cost, duration etc.) and/or outcomes of different treatments to facilitate decision making; thereby facilitating visual comparison of the efficacy of different treatments. As another example, the predicted cost, duration, and/or any other relevant parameters for each treatment solution can be displayed and compared, e.g., in list or table format. Examiner Note: Solutions are the restorative decision recommendation from the model (algorithm).) Examiner Note: “clinician-tunable” parameters is not defined in the specification, one of ordinary skill interprets the term as parameters for treatment (cost, solutions)..
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kopelman and Mason as each invention in the field of dentistry with the use of machine learning processing of image data. Adding the teaching of Mason provide an interface displaying multiple outcomes of different treatment solutions. One of ordinary skill in the art would have been motivated to make these modification to improve predicting and proactively correcting dental or orthodontic conditions.
Regarding dependent claim 20, depends on claim 19, Kopelman teaches: wherein the trained machine learning model is adapted to compute or estimate a volume of restorative material present on or in a tooth in the image data, (Kopelman – [0122] The conveyed information may include classification of an AOI, a size of an AOI and/or an importance rank of an AOI. For example the size of a crack tooth. [0096] In some embodiments, the dental condition identifiers 174 may perform analysis of a dentition feature using machine learning algorithms. For example, a dental condition profile 192 may be trained based on reference data 190 to correlate dentition features in the reference data 190 with associated clinical diagnosis of AOIs. [0191] Fig. 12A and 12B The indicators 1225 shown in FIG. 12A highlight specific areas of tooth wear in a color that contrasts with the tooth color. In some embodiments, the AR system may select the color based on the AOI being tooth wear and another color could indicate another type of AOI.)
and wherein deriving the plurality of parameters comprises computing at least one of a restorative volume or a surface proportion from the estimated volume of restorative material. (Kopelman – [0096] In some embodiments, the dental condition identifiers 174 may perform analysis of a dentition feature using machine learning algorithms. For example, a dental condition profile 192 may be trained based on reference data 190 to correlate dentition features in the reference data 190 with associated clinical diagnosis of AOIs.)
Regarding dependent claim 21, depends on claim 19, Kopelman teaches: wherein the trained machine learning model is adapted to receive image data corresponding to different imaging modalities as input, (Kopelman – [0101] The prior data comparator 180 may identify one or more areas of interest by comparing image data 162 to prior image data included in previous patient data 188. Patient data 188 may include past data regarding the patient (e.g., medical records), previous or current scanned images or models of the patient, current or past X-rays, 2D intraoral images, 3D intraoral images, virtual 2D models, virtual 3D models, or the like.)
and wherein the different imaging modalities are independently selected from an intraoral scan, a radiograph, or a cone-beam computed tomography (CBCT) scan. (Kopelman – [0198] the update image data may include a CT scan, x-ray image data, or other image data than was received in block 1410.)
Regarding independent claim 22, Kopelman teaches: A dental diagnostics system comprising:
a memory; and a processing device to execute instructions from the memory to perform a method comprising: (Kopelman − Fig. 30. [0055] Computing device 105 may include a processing device, memory, secondary storage, one or more input devices (e.g., such as a keyboard, mouse, tablet, speakers, or the like), one or more output devices (e.g., a display, a printer, etc.), and/or other hardware components. [0149] The methods depicted in FIGS. 2-29 may be performed by a processing logic that may comprise hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (e.g., instructions run on a processing device to perform hardware simulation), or a combination thereof.)
receiving image data of an intraoral cavity of a patient, (Kopelman − [abstract] The computing device receives the intraoral images from the intraoral scanner [0043] stream of images of a patient's oral cavity and dental arch and determine an area of interest present in the image data. )
the image data corresponding to one or more imaging modalities; (Kopelman − [0101] current X-rays, 2D intraoral images, 3D intraoral images or the like. (CT) scan)
deriving a plurality of parameters from the image data; (Kopelman − [0092] The dental condition identifiers 174 may operate on the original unprocessed image data 162 or may operate on processed image data that has been processed by the dental arch/oral cavity identifier 166 and/or the dental arch segmenter 172. For example, one dental condition identifier 174 may be a broken tooth identifier, which may separately perform broken tooth identification for each tooth identified by dental arch segmenter 172. Examples of dental condition identifiers 174 include a broken tooth identifier, a plaque identifier, a tooth wear identifier, an oral cancer identifier, a gum discoloration identifier, a tooth discoloration identifier, a malocclusion identifier, a gum recession identifier, a swollen gum identifier, and so on.) condition identifiers are the parameters.
applying a decision model to the plurality of parameters; (Kopelman − [0095-0097] [0095] the dental condition identifiers 174 may apply rules (e.g., including algorithms, models and/or profiles) that compare dentition features (also referred to as dental features) from a received image to reference data 190, which may include a store of dentition features. [0096] the dental condition identifiers 174 may perform analysis of a dentition feature using machine learning algorithms. For example, a dental condition profile 192 may be trained based on reference data 190 to correlate dentition features in the reference data 190 with associated clinical diagnosis of AOIs.) algorithms, models, and/or profiles is decision model, dental condition identifiers are the parameters.
and generating a restorative decision recommendation based on an output of the decision model to inform a treatment decision for the dental patient. (Kopelman − [0046] In some embodiments, the AR system may provide interactive feedback or other updated information to the dental practitioner based on an interaction with the patient. For example, the feedback may be provided during an intra-oral treatment such as a dental procedure. In some embodiments, the AR system may output to a display of the AR system recommended steps to take during an implant procedure, drilling procedure, grinding procedure, etc. For example, the AR system may show where to remove material for an insertion path, potential undercuts of neighboring teeth, placement of a hole for an implant, drilling depth, drilling direction, or the like. Similarly, the AR system may provide an indication of material to remove during interproximal reduction. [0203] FIG. 15A illustrates an example portion 1510 of a view of an AR display showing a visual overlay with an indication 1515 of an amount of tooth to remove in an interproximal region between two teeth.)
Kopelman does not explicitly teach: wherein the decision model comprises one or more clinician-tunable parameters that define conditions for determining which type of restorative decision recommendation is generated.
However, Mason teaches: wherein the decision model comprises one or more clinician-tunable parameters that define conditions for determining which type of restorative decision recommendation is generated. (Mason − Fig. 11A-G [0181] list of potential treatment options or solutions for the conditions 1110, [0193] Optionally, if multiple treatment solutions are available, the interface 1100 can include tools for comparing the characteristics (e.g., cost, duration etc.) and/or outcomes of different treatments to facilitate decision making; thereby facilitating visual comparison of the efficacy of different treatments. As another example, the predicted cost, duration, and/or any other relevant parameters for each treatment solution can be displayed and compared, e.g., in list or table format. Examiner Note: Solutions are the restorative decision recommendation from the model (algorithm).) Examiner Note: “clinician-tunable” parameters is not defined in the specification, one of ordinary skill interprets the term as parameters for treatment (cost, solutions)..
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kopelman and Mason as each invention in the field of dentistry with the use of machine learning processing of image data. Adding the teaching of Mason provide an interface displaying multiple outcomes of different treatment solutions. One of ordinary skill in the art would have been motivated to make these modification to improve predicting and proactively correcting dental or orthodontic conditions.
Regarding dependent claim 23, depends on claim 22, Kopelman teaches: An intraoral scanning system comprising: an intraoral scanner; (Kopelman − [abstract] The computing device receives the intraoral images from the intraoral scanner [0043] stream of images of a patient's oral cavity and dental arch and determine an area of interest present in the image data. ) and the dental diagnostics system of claim 22. (Kopelman − [0045] In some embodiments, the AR system may provide information to the dental practitioner based on analysis of the patient and/or in view of previous patient data. For example, the AR system may compare images or models from a previous visit to current images of the patient's dental arch. The AR system may then determine one or more areas of interest based on the comparison. For example, the AR system may identify changes since a last scan, analysis of wear over time, feedback on orthodontic treatment, or other analysis of changes)
Regarding dependent claim 24, depends on claim 1, Kopelman teaches: A non-transitory computer-readable storage medium having instructions encoded thereon that, when executed by a computing device, cause the computing device to perform the method of claim 1. (Kopelman − [0286] The data storage device 3028 may include a machine-readable storage medium (or more specifically a non-transitory computer-readable storage medium) 3024 on which is stored one or more sets of instructions 3026 embodying any one or more of the methodologies or functions described herein, such as instructions for an AR processing module 3050.)
Claim(s) 3 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Kopelman and Mason as applied to claim 1 above, and further in view of Saphier (US 20210321872 A1, Pub Date: Oct. 21, 2021).
Regarding dependent claim 3, depends on claim 2, Kopelman teaches: wherein the one or more imaging modalities comprises the intraoral scan, (Kopelman − [abstract] The computing device receives the intraoral images from the intraoral scanner [0043] stream of images of a patient's oral cavity and dental arch and determine an area of interest present in the image data. )
Kopelman teaches identifying multiple points in each images (e.g., point clouds) (see [0083]) but does not explicitly teach: and at least one of two-dimensional (2D) near infrared (NIR) images, 2D ultraviolet images, or 2D color images.
However, Saphier teaches: and wherein the image data comprises one or more three-dimensional (3D) point clouds ([0217] For instance, the scanner 150 may provide an intraoral scan as one or more point clouds. The intraoral scans may each comprise height information (e.g., a height map that indicates a depth for each pixel). [0220] intraoral scanning may include 3D data.)
and at least one of two-dimensional (2D) near infrared (NIR) images, 2D ultraviolet images, or 2D color images. (Saphier − [0215] [0220] The dental information (intraoral scan data 135A-N) generated from the intraoral scanning may include 3D scan data, 2D color images, NIRI and/or infrared images, and/or ultraviolet images, of all or a portion of the upper jaw and/or lower jaw. [0242] Images (e.g., color images))
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kopelman, Mason and Saphier as each invention in the field of dentistry with the use of machine learning processing of image data. Adding the teaching of Saphier provide an automatically determining and selecting one or more intraoral scans. One of ordinary skill in the art would have been motivated to make these modification to improve accuracy of a start and stop decision for intraoral scanning.
Regarding dependent claim 7, depends on claim 6, Kopelman does not explicitly teach:
However, Saphier teaches: wherein the geometric parameter comprises an inter-cuspal width. (Saphier − [0521] In one embodiment, processing logic compares spatial information from the first plurality of intraoral scans and/or the first 3D surface with spatial information from the one or more additional intraoral scans and/or the second 3D surface. feature detection (e.g., detecting tooth cusps) Examiner NOTE: Intercuspal width (distance) is distance between opposing tooth cusps.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kopelman, Mason and Saphier as each invention in the field of dentistry with the use of machine learning processing of image data. Adding the teaching of Saphier provide an automatically determining and selecting one or more intraoral scans. One of ordinary skill in the art would have been motivated to make these modification to improve accuracy of a start and stop decision for intraoral scanning.
Claim(s) 14 is rejected under 35 U.S.C. 103 as being unpatentable over Kopelman and Mason as applied to claim 1 above, and further in view of Bergman (US 20210192726 A1, Pub Date: Jun. 24, 2021).
Regarding dependent claim 14, depends on claim 1, Kopelman does not explicitly teach: a key performance indicator (KPI) database
Bergman teaches: further comprising: storing, in a record associated with a dentist to whom the restorative decision recommendation was provided, the restorative decision recommendation and an actual restorative decision made by the dentist in a key performance indicator (KPI) database. (Bergman – [0067] care information is stored in database 420 [0069] The practice management service (EHR) 608 also compiles the performance metrics for the dentists in the organization and prepares a report for viewing and analysis by a quality analyst or auditor 612. The quality analyst or auditor may use the same or a different computer as computer 606 in the dental office.)
Accordingly, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teaching of Kopelman, Mason and Bergman as each invention in the field of dentistry with the use of machine learning processing of image data. Adding the teaching of Bergman provide a performance metrics process for dentistry organization. One of ordinary skill in the art would have been motivated to improve the quality of diagnostic regarding restorative treatment from intraoral imaging data.
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 CARL E BARNES JR whose telephone number is (571)270-3395. The examiner can normally be reached Monday-Friday 9am-6pm.
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/CARL E BARNES JR/Examiner, Art Unit 2178
/STEPHEN S HONG/Supervisory Patent Examiner, Art Unit 2178
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