tevDETAILED 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 .
Priority
Receipt is acknowledged of certified copies of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 05/04/2023, 09/26/2023, and 02/14/2024 have been considered by the examiner.
Claim Objections
Claim 1 is objected to because of the following informalities:
In claim 1, line 2, “distinguishing, in a 3D model,” should read “distinguishing, in a three-dimensional (3D) model,” to provide the proper acronym expansion prior to reciting the acronym.
Appropriate correction is required.
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 1-15 are rejected under 35 U.S.C. 101.
Regarding independent claim 1 and its dependent claims 2-15,
Step 1 Analysis: Claim 1 is directed to a method, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part:
“distinguishing,… an analysis region including at least one tooth region; and determining a degree of completeness… based on the analysis region.”
The limitations, as drafted above, are processes that, under broadest reasonable interpretation (BRI) cover the performance of the limitation in the mind which falls within the ”Mental Processes” grouping of abstract ideas. The limitations of:
“distinguishing… an analysis region including at least one tooth region” is a step, under BRI, that a human can also perform through mental processes such as observation/evaluation and judgement. For instance, a human can observe an existing model and identify a specific region including a tooth.
“and determining a degree of completeness… based on the analysis region” is a step, under BRI, that a human can perform through mental processes of observation/evaluation and judgment. For instance, a human can determine if a given anatomical/dental model is complete based on the identified region of an existing model.
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional element(s) –
“in a 3D model”
“of the 3D model”
The additional element “3D model” is a step of insignificant extra-solution/post solution activity of data generating/outputting wherein the subject matter being evaluated is a 3D model.
Accordingly, 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 claim as a whole is directed to an abstract idea.. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea.
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 2-15 do not provide elements that overcome the deficiencies of the independent claim 1.
Moreover, claim 2 recites, in part, “before distinguishing the analysis region…; and acquiring the 3D model after setting the analysis region” which are elements including steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc. Claim 2 further recites, in part, “setting the analysis region” which is a step that, under BRI, could be performed by the human mind through mental processes such as observation/evaluation and judgement; for instance, a human (ex: dental professional) can determine a specific region to be an analysis region.
Claim 3 recites, in part, “before distinguishing the analysis region, acquiring the 3D model” which are elements including steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc. Claim 3 further recites, in part, “and setting the analysis region in the acquired 3D model” which is a step that, under BRI, could be performed by the human mind through mental processes such as observation/evaluation and judgement; for instance, a human (ex: dental professional) can determine a specific region to be an analysis region in an existing model.
Claims 4, 7, 11-13 recite, in part, wherein clauses of merely further specification of the elements which they depend on, therefore, not an indication of an integration of the abstract idea into a practical application, nor considered significantly more.
Claim 5 recites, in part, “wherein the 3D model is distinguished into the tooth region representing teeth and a gingival region representing gingivae through at least one of color information and curvature information” which is a step that, under BRI, could be performed by the human mind through mental processes such as observation/evaluation and judgement; for instance, a human (ex: a dental professional) can determine which section is a tooth region and which section is a gingival region based on observed color or curvature.
Claim 6 recites, in part, “the tooth region is distinguished into individual tooth regions representing individual teeth according to a dental formula distinguishing criterion including surface curvature information of a tooth” which is a step that, under BRI, can be performed by the human mind through mental processes such as observation/evaluation and judgement; for instance, a human (ex: a dental professional) can look at scan data of teeth and mentally distinguish individual teeth using their visual observation of surface curvature based on standard dental formula criterion.
Claim 8 recites, in part, “the 3D model is determined to be complete when an area or a volume ratio of an attention region to the analysis region is less than a predetermined ratio” which are elements including steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc.
Claim 9 recites, in part, “the attention region includes at least one of a blank region in the 3D model for which scan data is not input and a low-density region in the 3D model for which scan data is input as being below a predetermined threshold density” which is a step that, under BRI, can be performed by the human mind through mental processes such as observation/evaluation and judgement; for instance, a human (ex: a dental professional) can look at scan data and identify that a region is blank/incomplete.
Claim 10 recites, in part, “the degree of completeness is determined based on a threshold value different for each individual tooth region of the 3D model” which is a step that, under BRI, can be performed by the human mind through mental processes such as observation/evaluation and judgement; for instance, a human (ex: a dental professional) can look at scan data and identify that it is complete/incomplete based on the observed scan of each tooth.
Claim 14 recites, in part, “generating a feedback to a user based on the result of determining the degree of completeness of the 3D model” which are elements including steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc.
Claim 15 recites, in part, “wherein the attention region is fed back to a user” which are elements including steps of insignificant extra-solution/post-solution activities of data gathering, data generating, data transmitting, etc.
Accordingly, the dependent claims 2-15 do not provide elements that overcome the deficiencies of the independent claim 1 and thus, are not patent eligible under 35 U.S.C. 101.
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 of this title, 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 1-3 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over KOPELMAN (US 20240423476 A1), hereinafter referenced as KOPELMAN in view of LEVIN (US 20210287571 A1), hereinafter referenced as LEVIN.
Regarding claim 1, KOPELMAN teaches a data processing method (Figs. 2A-3C, Paragraph [0070] – KOPELMAN discloses FIGS. 2A-3C illustrate flow diagrams of methods for performing intraoral scans of dental sites for patients.), comprising:
distinguishing, in a 3D model, an analysis region (Fig. 2A-B, Paragraph [0037] – KOPELMAN discloses AOI identifying module 115 is responsible for identifying areas of interest (AOIs) [wherein areas of interest are an analysis region] from intraoral scan data (e.g., intraoral images) and/or virtual 3D models generated from intraoral scan data. Paragraph [0081] – KOPELMAN discloses at block 260, processing logic identifies one or more voxels from the intraoral images and/or the virtual models that satisfy a criterion. Paragraph [0083-0084] – KOPELMAN discloses if any candidate intraoral areas of interest are verified as actual intraoral areas of interest, the method continues to block 280. At block 280, classifications are determined for the intraoral areas of interest. For example, AOIs may be classified as voids, conflicting surfaces, changes in a dental site, foreign objects, and so forth.)
including at least one tooth region (Fig. 2A-B, Paragraph [0037] – KOPELMAN discloses areas of interest may include voids (e.g., areas for which scan data is missing), areas of conflict or flawed scan data (e.g., areas for which overlapping surfaces of multiple intraoral images fail to match), areas indicative of foreign objects (e.g., studs, bridges, etc.), areas indicative of tooth wear, areas indicative of tooth decay, areas indicative of receding gums, unclear gum line, unclear patient bite, unclear margin line (e.g., margin line of one or more preparation teeth), and so forth. See also Fig. 5A-B, Paragraphs [0130-0131].);
KOPELMAN fails to explicitly teach and determining a degree of completeness of the 3D model based on the analysis region.
However, LEVIN explicitly teaches and determining a degree of completeness of the 3D model based on the analysis region (Fig. 18C-D, Paragraph [0076] – LEVIN discloses after the subscans [wherein a subscan is a region] are completed, a training-session three-dimensional digital model is computed from those images captured during the training scan that would have been captured during a normal scanning procedure and displayed in the model window of the user input to the trainee, in step 1840. LEVIN further discloses all of the displayed information, including the generated three-dimensional digital model and results provide various types of feedback to the trainee to indicate how well the trainee accomplished the goal of the current training session. These metrics may include degree of coverage, degree of completion of the three-dimensional digital model, degree of accuracy of the three-dimensional digital model, suggestions for improving efficiency and accuracy in the trainee's scanning techniques, and many other types of information.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; with the teachings of LEVIN having and determining a degree of completeness of the 3D model based on the analysis region.
Wherein having KOPELMAN’s data processing method wherein having and determining a degree of completeness of the 3D model based on the analysis region.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy along with providing feedback metrics, since both KOPELMAN and LEVIN relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and LEVIN discloses methods and systems that provide semi-automated and automated training to technicians who use oral-cavity-imaging-and-modeling systems to efficiently and accurately generate three-dimensional models of patients' teeth and underlying tissues; the generated three-dimensional digital model and results provide various types of feedback to the trainee to indicate how well the trainee accomplished the goal of the current training session. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and LEVIN (US 20210287571 A1), Paragraph [0005, 0076].
Regarding claim 2, KOPELMAN in view of LEVIN teach the method of Claim 1,
KOPELMAN further teaches further comprising: before distinguishing the analysis region, setting the analysis region (Fig. 2A, Paragraph [0072] – KOPELMAN discloses at block 215, processing logic identifies one or more candidate intraoral areas of interest from the first intraoral image. Paragraph [0075] – KOPELMAN further discloses at block 235, processing logic determines whether the candidate intraoral areas of interest from the first intraoral image are verified as intraoral areas of interest.);
and acquiring the 3D model after setting the analysis region (Fig. 2A, Paragraph [0078] – KOPELMAN discloses at block 248, a virtual 3D model of the dental site is generated. The virtual model 3D may be generated as discussed above).
Regarding claim 3, KOPELMAN in view of LEVIN teach the method of Claim 1,
KOPELMAN further teaches further comprising: before distinguishing the analysis region, acquiring the 3D model (Fig. 2B, Paragraph [0080] – KOPELMAN discloses at block 255, intraoral images of a dental site are received. Additionally, or alternatively, one or more virtual models of a dental site may be received. See also Paragraphs [0083-0084].);
and setting the analysis region in the acquired 3D model (Fig. 2B, Paragraph [0082] – KOPELMAN discloses at block 265, one or more subsets of the identified voxels that are in close proximity to one another are identified. At block 270, these subsets are grouped into candidate intraoral areas of interest [wherein grouping candidate areas is setting the analysis region].).
Regarding claim 14, KOPELMAN in view of LEVIN teach the method of Claim 1,
KOPELMAN fails to explicitly teach further comprising: generating a feedback to a user based on the result of determining the degree of completeness of the 3D model.
However, LEVIN explicitly teaches further comprising: generating a feedback to a user based on the result of determining the degree of completeness of the 3D model (Fig. 18D, Paragraph [0076] – LEVIN discloses all of the displayed information, including the generated three-dimensional digital model and results provide various types of feedback to the trainee to indicate how well the trainee accomplished the goal of the current training session. These metrics may include degree of coverage, degree of completion of the three-dimensional digital model, degree of accuracy of the three-dimensional digital model, suggestions for improving efficiency and accuracy in the trainee's scanning techniques, and many other types of information.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of LEVIN having further comprising: generating a feedback to a user based on the result of determining the degree of completeness of the 3D model.
Wherein having KOPELMAN’s data processing method further comprising: generating a feedback to a user based on the result of determining the degree of completeness of the 3D model.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy along with providing feedback metrics, since both KOPELMAN and LEVIN relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and LEVIN discloses methods and systems that provide semi-automated and automated training to technicians who use oral-cavity-imaging-and-modeling systems to efficiently and accurately generate three-dimensional models of patients' teeth and underlying tissues; the generated three-dimensional digital model and results provide various types of feedback to the trainee to indicate how well the trainee accomplished the goal of the current training session. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and LEVIN (US 20210287571 A1), Paragraph [0005, 0076].
Claims 4 and 5 are rejected under 35 U.S.C. 103 as being unpatentable over KOPELMAN (US 20240423476 A1), hereinafter referenced as KOPELMAN in view of LEVIN (US 20210287571 A1), hereinafter referenced as LEVIN in further view of CUNLIFFE (US 20190254588 A1), hereinafter referenced as CUNLIFFE.
Regarding claim 4, KOPELMAN in view of LEVIN teach the method of Claim 1,
KOPELMAN in view of LEVIN fail to explicitly teach wherein the tooth region includes an entire tooth region of the 3D model.
However, CUNLIFFE explicitly teaches wherein the tooth region includes an entire tooth region of the 3D model (Fig. 3, Paragraph [0022] – CUNLIFFE discloses segmenting the teeth from the gingiva for steps 26 and 28 involves detecting the gum line in the 3D scans to digitally identify the boundary between the teeth and gingiva to generate a gingiva segmented digital 3D model. Paragraph [0024] – CUNLIFFE further discloses Input: a 3D mesh representing a tooth with gums. Output: A subset of the input 3D mesh representing only the tooth.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of CUNLIFFE having wherein the tooth region includes an entire tooth region of the 3D model.
Wherein having KOPELMAN’s data processing method wherein the tooth region includes an entire tooth region of the 3D model.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy along with providing a way to track changes over time, since both KOPELMAN and CUNLIFFE relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and CUNLIFFE discloses methods for tracking gum line changes by comparing digital 3D models of teeth and gingiva taken at different times. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and CUNLIFFE (US 20190254588 A1), Paragraph [0002-0004].
Regarding claim 5, KOPELMAN in view of LEVIN teach the method of Claim 1,
KOPELMAN in view of LEVIN fail to explicitly teach wherein the 3D model is distinguished into the tooth region representing teeth and a gingival region representing gingivae through at least one of color information and curvature information.
However, CUNLIFFE explicitly teaches wherein the 3D model is distinguished into the tooth region representing teeth and a gingival region representing gingivae through at least one of color information and curvature information (Fig. 3, Paragraph [0022]- CUNLIFFE discloses segmenting the teeth from the gingiva for steps 26 and 28 involves detecting the gum line in the 3D scans to digitally identify the boundary between the teeth and gingiva to generate a gingiva segmented digital 3D model. This digital identification for segmentation can include, for example, digitally separating the teeth from the gingiva, or using a curve or other indicia on the digital 3D model to distinguish between the teeth from the gum.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of CUNLIFFE having wherein the 3D model is distinguished into the tooth region representing teeth and a gingival region representing gingivae through at least one of color information and curvature information.
Wherein having KOPELMAN’s data processing method wherein the 3D model is distinguished into the tooth region representing teeth and a gingival region representing gingivae through at least one of color information and curvature information.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy along with providing a way to track changes over time, since both KOPELMAN and CUNLIFFE relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and CUNLIFFE discloses methods for tracking gum line changes by comparing digital 3D models of teeth and gingiva taken at different times. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and CUNLIFFE (US 20190254588 A1), Paragraph [0002-0004].
Claims 6 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over KOPELMAN (US 20240423476 A1), hereinafter referenced as KOPELMAN in view of LEVIN (US 20210287571 A1), hereinafter referenced as LEVIN in further view of FUJIWARA (US 20170367789 A1), hereinafter referenced as FUJIWARA.
Regarding claim 6, KOPELMAN in view of LEVIN teach the method of Claim 1,
KOPELMAN in view of LEVIN fail to explicitly teach wherein in distinguishing the analysis region, the tooth region is distinguished into individual tooth regions representing individual teeth according to a dental formula distinguishing criterion including surface curvature information of a tooth.
However, FUJIWARA explicitly teaches wherein in distinguishing the analysis region, the tooth region is distinguished into individual tooth regions representing individual teeth (Fig. 20-21, Paragraph [0080] – FUJIWARA discloses a process for extracting, for each tooth, a vertex group defining a tooth crown shape of at least one tooth from the plurality of vertexes on the basis of the normal vectors or the curvatures acquired in the process A2.)
according to a dental formula distinguishing criterion including surface curvature information of a tooth (Fig. 20-21, Paragraph [0184] – FUJIWARA discloses the tooth crown type decision unit 24 automatically determines allocation of the FDI numbers to the most likely tooth crown segments from a great number of FDI number candidates on the basis of such decision conditions as given above. See also Paragraph [0080].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of FUJIWARA having wherein in distinguishing the analysis region, the tooth region is distinguished into individual tooth regions representing individual teeth according to a dental formula distinguishing criterion including surface curvature information of a tooth.
Wherein having KOPELMAN’s data processing method wherein in distinguishing the analysis region, the tooth region is distinguished into individual tooth regions representing individual teeth according to a dental formula distinguishing criterion including surface curvature information of a tooth.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy, since both KOPELMAN and FUJIWARA relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and FUJIWARA discloses a tooth crown information acquisition method wherein by removing candidates other than those of the inputted tooth crown segments from a target of the decision, the determination process of an FDI number can be executed within a practical time period and with a sufficiently high degree of accuracy. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and FUJIWARA (US 20170367789 A1), Paragraph [0184].
Regarding claim 7, KOPELMAN and LEVIN in view of FUJIWARA teach the method of Claim 6,
KOPELMAN in view of LEVIN fail to explicitly teach wherein the dental formula distinguishing criterion further includes at least one of size information and shape information of the tooth.
However, FUJIWARA explicitly teaches wherein the dental formula distinguishing criterion further includes at least one of size information and shape information of the tooth (Fig. 5, Paragraph [0072] – FUJIWARA discloses the tooth crown database 33 registers and stores a great amount of tooth crown shape information acquired from an unspecified number of people therein for individual cases, and has a folder (record) for each case, for example, as depicted in FIG. 5. An FDI number corresponding to a type of a tooth crown (11, 12, 13, 14, . . . , and 44 in FIG. 5) is applied as a file name to each file. See also paragraph [0016, 0090].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of FUJIWARA having wherein the dental formula distinguishing criterion further includes at least one of size information and shape information of the tooth.
Wherein having KOPELMAN’s data processing method wherein the dental formula distinguishing criterion further includes at least one of size information and shape information of the tooth.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy, since both KOPELMAN and FUJIWARA relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and FUJIWARA discloses a tooth crown information acquisition method wherein by removing candidates other than those of the inputted tooth crown segments from a target of the decision, the determination process of an FDI number can be executed within a practical time period and with a sufficiently high degree of accuracy. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and FUJIWARA (US 20170367789 A1), Paragraph [0184].
Claims 8, 9, 12, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over KOPELMAN (US 20240423476 A1), hereinafter referenced as KOPELMAN in view of LEVIN (US 20210287571 A1), hereinafter referenced as LEVIN in further view of KRIEGEL et al. (Kriegel, Simon, et al. "Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects." Journal of Real-Time Image Processing 10.4 (2015): 611-631.), hereinafter referenced as KRIEGEL.
Regarding claim 8, KOPELMAN in view of LEVIN teach the method of Claim 1,
KOPELMAN in view of LEVIN fail to explicitly teach wherein in determining the degree of completeness, the 3D model is determined to be complete when an area or a volume ratio of an attention region to the analysis region is less than a predetermined ratio.
However, KRIEGEL explicitly teaches wherein in determining the degree of completeness (Page 613, Col. 2, Section 2.5, Lines [11-14] – KRIEGEL discloses a complete and autonomous robotic system for real-time 3D modeling of unknown objects which aborts after a defined mesh quality is reached.),
the 3D model is determined to be complete when an area or a volume ratio of an attention region to the analysis region is less than a predetermined ratio (Page 624, Col. 1, Lines [21-27] – KRIEGEL discloses in this work, we determine the surface area Afilled of all triangles in the mesh and estimate the surface area for each hole individually, which is summed up to the total area Aempty of all holes. The mesh coverage is estimated by:
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Page 624, Col. 2, Lines [22-24] – KRIEGEL further discloses the algorithm aborts [wherein aborts is determining completion] if a certain mesh coverage
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Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of KRIEGEL having wherein in determining the degree of completeness, the 3D model is determined to be complete when an area or a volume ratio of an attention region to the analysis region is less than a predetermined ratio.
Wherein having KOPELMAN’s data processing method wherein in determining the degree of completeness, the 3D model is determined to be complete when an area or a volume ratio of an attention region to the analysis region is less than a predetermined ratio.
The motivation behind the modification would have been to obtain a data processing method with improved speed and accuracy and enhanced performance, since both KOPELMAN and KRIEGEL relate to 3D imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and KRIEGEL discloses an autonomous 3D modeling system, consisting of an industrial robot and a laser striper, for efficient surface reconstruction of unknown objects is presented; 3D models are obtained a lot faster and also the quality (completeness rate and point density) of the surface model is higher. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and KRIEGEL (Kriegel, Simon, et al. "Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects." Journal of Real-Time Image Processing 10.4 (2015): 611-631.), Page 629, Col. 1.
Regarding claim 9, KOPELMAN and LEVIN in view of KRIEGEL teach the method of Claim 8,
KOPELMAN further teaches wherein the attention region includes at least one of a blank region in the 3D model for which scan data is not input and a low-density region in the 3D model for which scan data is input as being below a predetermined threshold density (Fig. 5B & 6, Paragraph [0037] – KOPELMAN discloses AOI identifying module 115 is responsible for identifying areas of interest (AOIs) from intraoral scan data (e.g., intraoral images) and/or virtual 3D models generated from intraoral scan data. Such areas of interest may include voids (e.g., areas for which scan data is missing). Paragraph [0052] – KOPELMAN further discloses multiple areas of interest 562, 564, 566, 568, 570, 572 are also shown in the image of the dental arch 550. These areas of interest 562, 564, 566, 568, 570, 572 represent missing scan data that satisfies a clinical importance criterion (e.g., intraoral areas of interest greater than a threshold size or having one or more dimensions that violate a geometric criterion). Paragraph [0134] – KOPELMAN further discloses the dental arch includes multiple voids based on incomplete scan data. Such voids are one type of intraoral area of interest that is called out by flags 612-624.).
Regarding claim 12, KOPELMAN and LEVIN in view of KRIEGEL teach the method of Claim 9,
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Greyscale
Annotated diagram of KOPELMAN Fig. 5A illustrating area(s) of interest (ex: 515 representing missing scan data) on dental scan image
KOPELMAN further teaches wherein the blank region includes an inner closed-loop region created by boundaries of scan data constituting the 3D model (Fig. 5A, Paragraph [0085] – KOPELMAN discloses an indicator may identify an intraoral area of interest as representing a void or insufficient image data. Paragraph [0130] – KOPELMAN discloses the image of the dental arch 500 may be constructed from one or more scans of a physical model of a dental arch. These areas of interest 509, 515, 525 represent missing scan data that satisfies a clinical importance criterion. See annotated Fig. 5A above for reference, also Paragraph [0134].).
Regarding claim 15, KOPELMAN and LEVIN in view of KRIEGEL teach the method of Claim 8,
KOPELMAN further teaches wherein the attention region is fed back to a user (Fig. 6, Paragraph [0100] – KOPELMAN discloses processing logic may employ the importance ranks of indications in suggesting a rescan order for one or more indications (e.g., indications regarding scan assistance such as indications concerning missing and/or flawed scan data) and/or in suggesting a practitioner attention order for one or more indications (e.g., indications regarding diagnostic assistance and/or indications regarding foreign object recognition assistance). See also Paragraphs [0133-0134].).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over KOPELMAN (US 20240423476 A1), hereinafter referenced as KOPELMAN in view of LEVIN (US 20210287571 A1), hereinafter referenced as LEVIN in further view of KRIEGEL et al. (Kriegel, Simon, et al. "Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects." Journal of Real-Time Image Processing 10.4 (2015): 611-631.), hereinafter referenced as KRIEGEL in further view of PARAKETSOV (US 20220079714 A1), hereinafter referenced as PARAKETSOV.
Regarding claim 10, KOPELMAN and LEVIN in view of KRIEGEL teach the method of Claim 8,
KOPELMAN and LEVIN in view of KRIEGEL fail to explicitly teach wherein in determining the degree of completeness, the degree of completeness is determined based on a threshold value different for each individual tooth region of the 3D model.
However, PARAKETSOV explicitly teaches wherein in determining the degree of completeness, the degree of completeness is determined based on a threshold value different for each individual tooth region of the 3D model (Fig. 1D, Paragraph [0067] – PARAKETSOV discloses similarity comparison engine 184 may implement one or more automated agents configured to access the quality of the segmentation result of the current 3D dental model. For example, the 2D and/or 3D dimensions of each tooth can be compared to the 2D and/or 3D dimensions of each corresponding tooth, and the similarity comparison engine 184 can determine if the shape of each tooth in the 3D dental model falls within an acceptable threshold when compared to the shape of its corresponding tooth from the prior 3D dental models. If, for example, the comparisons are within an acceptable threshold, then the segmentation can be deemed to be acceptable. Alternatively, if the comparison falls outside of an acceptable threshold, then the segmentation can be deemed unacceptable, and steps can be taken to either re-scan the patient, re-do the segmentation, or have a trained technician manually segment the 3D dental model. See also Figs. 5A-B, Paragraph [0116].).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of PARAKETSOV having wherein in determining the degree of completeness, the degree of completeness is determined based on a threshold value different for each individual tooth region of the 3D model.
Wherein having KOPELMAN’s data processing method wherein in determining the degree of completeness, the degree of completeness is determined based on a threshold value different for each individual tooth region of the 3D model.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy along with reduced processing time, since both KOPELMAN and PARAKETSOV relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and PARAKETSOV discloses apparatuses (e.g., systems) and methods for assisting in generating and segmenting a 3D dental model of a subject's dentition; methods and apparatuses described herein may provide a significant improvement in the processing time and therefore reduction in associated costs (including in computing time). Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and PARAKETSOV (US 20220079714 A1), Paragraph [0109].
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over KOPELMAN (US 20240423476 A1), hereinafter referenced as KOPELMAN in view of LEVIN (US 20210287571 A1), hereinafter referenced as LEVIN in further view of KRIEGEL et al. (Kriegel, Simon, et al. "Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects." Journal of Real-Time Image Processing 10.4 (2015): 611-631.), hereinafter referenced as KRIEGEL in further view of RUBBERT (US 20060281041 A1), hereinafter referenced as RUBBERT.
Regarding claim 11, KOPELMAN and LEVIN in view of KRIEGEL teach the method of Claim 9,
KOPELMAN and LEVIN in view of KRIEGEL fail to explicitly teach wherein the blank region includes a hole region detected by aligning the 3D model with a pre-stored template and using an intersection test through at least one light beam generated from the surface of the template.
However, RUBBERT explicitly teaches wherein the blank region includes a hole region detected by aligning the 3D model with a pre-stored template (Fig. 58A-F, Paragraph [0290] – RUBBERT discloses the back office server workstation stores a three-dimensional virtual template tooth object for each tooth in the maxilla and the mandible. Paragraph [0303] – RUBBERT discloses the modeling algorithm will internally mark or classify each generated point in the virtual tooth model as being based on scan data, (true points), or if it has been constructed by the algorithm due to the lack of data (artificial points, supplied by the template tooth 310 in FIG. 58B).)
and using an intersection test through at least one light beam generated from the surface of the template (Figs. 58A-F, Paragraph [0290] – RUBBERT discloses the template tooth 310 is positioned approximately in the same location in space as the tooth 308. The template tooth is placed at the point cloud of the dentition according to the labial landmark 302. Paragraph [0291] – RUBBERT further discloses vectors are drawn from the points on the template tooth to the scanned point cloud of the tooth 308. Every ray intersects several surfaces, depending on how often the respective part of the surface has been covered during scanning.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of RUBBERT having wherein the blank region includes a hole region detected by aligning the 3D model with a pre-stored template and using an intersection test through at least one light beam generated from the surface of the template.
Wherein having KOPELMAN’s data processing method wherein the blank region includes a hole region detected by aligning the 3D model with a pre-stored template and using an intersection test through at least one light beam generated from the surface of the template.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy, since both KOPELMAN and RUBBERT relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and RUBBERT relates to a computerized method of creating individual, virtual, three-dimensional tooth models from three-dimensional information of a patient's dentition and a template object; individual virtual tooth objects are thus a highly useful tool in orthodontic treatment planning, monitoring, and diagnosis. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and RUBBERT (US 20060281041 A1), Paragraph [0004, 0013].
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over KOPELMAN (US 20240423476 A1), hereinafter referenced as KOPELMAN in view of LEVIN (US 20210287571 A1), hereinafter referenced as LEVIN in further view of KRIEGEL et al. (Kriegel, Simon, et al. "Efficient next-best-scan planning for autonomous 3D surface reconstruction of unknown objects." Journal of Real-Time Image Processing 10.4 (2015): 611-631.), hereinafter referenced as KRIEGEL in further view of PAGE (US 20180227570 A1), hereinafter referenced as PAGE.
Regarding claim 13, KOPELMAN and LEVIN in view of KRIEGEL teach the method of Claim 9,
KOPELMAN and LEVIN in view of KRIEGEL fail to explicitly teach wherein the low-density region is calculated based on at least one of a number of acquired scan data and a scan angle between the scan data.
However, PAGE explicitly teaches wherein the low-density region is calculated based on at least one of a number of acquired scan data and a scan angle between the scan data (Fig. 7, Paragraph [0111] – PAGE discloses in areas that are concave, recessed, or where materials or colors or lighting are less optimal for the scanning process, data may either not be acquired or data that is acquired may be of low quality (e.g. few data points per area (low density) and/or low accuracy). Paragraph [0119] – PAGE discloses a scanning camera (which may be hand-held) may capture many data sets per second (for example, between 2 and 100; in one example a desired value may be 10) and these datasets may be aligned in real-time or near-real-time to provide a net data density based on the total number of data points collected in a given area of the surface of the object being scanned divided by that area. Note that each dataset may in general be collected from a different camera pose (pose means angle and position) and therefore at a different angle to the area of the object being scanned.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of KOPELMAN in view of LEVIN of having a data processing method, comprising: distinguishing, in a 3D model, an analysis region including at least one tooth region; and determining a degree of completeness of the 3D model based on the analysis region, with the teachings of PAGE having wherein the low-density region is calculated based on at least one of a number of acquired scan data and a scan angle between the scan data.
Wherein having KOPELMAN’s data processing method wherein the low-density region is calculated based on at least one of a number of acquired scan data and a scan angle between the scan data.
The motivation behind the modification would have been to obtain an enhanced data processing method with improved speed and accuracy, since both KOPELMAN and PAGE relate to oral cavity imaging and modeling, wherein KOPELMAN relates to the field of intraoral scanning and, in particular, to a system and method for improving the results of intraoral scanning; the user may be notified of areas of interest that should be rescanned, the user may then rescan the areas of interest during the scan session; this can facilitate quick and accurate scan sessions, and PAGE relates to three-dimensional (3D) scanners that can be used to create digital 3D representations of physical objects; the present system uses computer vision algorithms and object recognition algorithms and machine learning (ML) and artificial intelligence (AI) to identify objects, environments and context around the objects being scanned wherein coupling of metadata with scan data can enable and improve many uses of the 3D scan data as well as improving manufacturing and assembly of physical components and systems. Please see KOPELMAN (US 20240423476 A1), Paragraph [0022], and PAGE (US 20180227570 A1), Paragraph [0064-0071].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
ELBAZ et al. (US 20250339245 A1) - Methods and apparatuses for generating and displaying a model of a subject's teeth. Described herein are intraoral scanning methods and apparatuses for generating a three-dimensional model of a subject's intraoral region (e.g., teeth). These methods and apparatuses may be used for identifying and evaluating lesions, caries and cracks in the teeth.… Fig. 1, Abstract.
JONES et al. (US 20040175671 A1) - A computer or other digital circuitry is used to assist in the creation of a digital model of an individual component, such as a tooth or gum tissue, in a patient's dentition. The computer receives a data set that forms a three-dimensional (3D) representation of the patient's dentition, applies a test to the data set to identify data elements that represent portions of the individual component, and creates a digital model of the individual component based upon the identified data elements. Many implementations require the computer to identify data elements representing a 2D cross-section of the dentition lying in a 2D plane that is roughly parallel to or roughly perpendicular to the dentition's occlusal plane. The computer analyzes the 2D cross-section to identify dentition features that represent boundaries between individual dentition components.… Fig. 1, Abstract.
PAVLOVSKAIA et al. (US 20040023188 A1) - A computer-implemented method separates gingiva from a model of a tooth by defining a cutting surface along the gingiva; and applying the cutting surface to the tooth to separate the gingiva from the tooth.… Fig. 1, Abstract.
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/BEZAWIT NOLAWI SHIMELES/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
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