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 .
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 1-5, 7-16, 18-19 are rejected under 35 U.S.C. 101 because:
the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Regarding Claim 1:
Step 1 – The claim is drawn to a “method of determining a bite setting” and is therefore a process.
Step 2A – The claim is drawn to an abstract idea. The abstract idea being a mental process. The limitations of:
• receiving first and unsegmented second digital jaw models
• determining a rough bite approximation
• determining one or more initial bite positions and one or more iterative bite positions
Determining one or more cusps
Performing penetration fixing iterations to resolve jaw penetration
• determining a score for each iterative bite position
• outputting the bite based on the score are all data identification and manipulation and these steps can be performed by a human mind (i.e., a mental process). The claim does not recite any additional elements that integrate the abstract idea into a practical application.
Step 2B- There are no further elements in the claim that amount to significantly more than the judicial exception (abstract idea). The method as disclosed is performed on a generic use computer. Therefore claim 1 is not eligible subject matter under 35 USC 101.
Regarding claims 2-5, 7-11, these claims do not integrate the abstract idea into a practical application and they do not recite additional elements that amount to significantly more than the judicial exception (abstract idea). These dependent claims merely recite further specifics of the data being processed in the independent claim or they recite further data identification and selection steps which themselves are an abstract idea.
Regarding Claims 12 and 19:
Step 1 – The claim is drawn to a “a system for determining a bite setting” and “a non-transitory computer readable medium” and both are therefore apparatuses.
Step 2A – The claim is drawn to an abstract idea. The abstract idea being a mental process. The limitations of:
• receiving first and second unsegmented digital jaw models
• determining a rough bite approximation
• determining one or more initial bite positions and one or more iterative bite positions
Determining one or more cusps
Performing penetration fixing iterations to resolve jaw penetration
• determining a score for each iterative bite position
• outputting the bite based on the score are all data identification and manipulation and these steps can be performed by a human mind (i.e., a mental process). The claim does not recite any additional elements that integrate the abstract idea into a practical application.
Step 2B- There are no further elements in the claim that amount to significantly more than the judicial exception (abstract idea). The steps as disclosed are performed on a generic use computer (i.e. the system or the storing medium). Therefore claims 12 and 19 are not eligible subject matter under 35 USC 101.
Regarding claims 13-16, 18 these claims do not integrate the abstract idea into a practical application and they do not recite additional elements that amount to significantly more than the judicial exception (abstract idea). These dependent claims merely recite further specifics of the data being processed in the independent claim or they recite further data identification and selection steps which themselves are an abstract idea.
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) 1-5, 7-16, 18-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chishti et al. (US 2002/0072027 A1), in view of Kitching et al. (US 2008/0306724 A1), Fisker et al. (US 2013/0066598 A1), and further in view of Imgrund (US 2002/0150859 A1).
Regarding claim 1, Chishti teaches a computer-implemented method of determining a bite setting (see at least the abstract and [0030], “determining the occlusion”), comprising:
receiving first and second digital jaw models (100, 101, figure 1);
determining a first digital jaw model center and a second digital jaw model center ([0058]; such that the midlines, which include centerpoints for each jaw are determined to be aligned);
determining a rough bite approximation of the first and second digital jaw models (step 202, Figure 3 and [0038]; “an initial data set (IDDS) representing an initial tooth arrangement is obtained” and being used as a starting position);
determining one or more initial bite positions of the first and second digital jaw models from the rough approximation (204, Figure 3 and [0038]; the IDDS is manipulated to produce a final digital data set (FDDS) corresponding to a desired tooth arrangement);
determining one or more iterative bite positions of the first and second digital jaw models for each of the one or more initial bite positions (206 Figure 3 and [0039]; both the IDDS and the FDDS are used to produce a plurality of intermediate digital data sets (INTDDs) to correspond to incrementally adjusted models);
determining one or more cusps on the first digital jaw model and one or more cusps on the second digital jaw model (see at least [0046], [0052-0059]; cusps of the teeth of the models are considered and used);
performing penetration fixing iterations to resolve jaw penetrations ([0040], [0046], [0066]; Chishti teaches iteratively generating subsequent data sets based on prior data sets until a final data set representing an acceptable tooth arrangement is achieved),
determining a score for each iterative bite position ([0047-0048]). Chishti teaches a process 300 is an optimization process that follows process 200. For optimization of the previously designed positions, an index is computed to identify the level of malocclusion and how far a tooth is from good occlusion at different bite representations (iterative bite positions). A score is assigned to various occlusal traits which make up a malocclusion. The scores of each position are summed to obtain an overall total, representing the degree a case deviates from normal alignment and occlusion. A score of zero indicates good alignment while a higher score indicates increased levels of irregularity ([0047]);
outputting the bite setting based on the score ([0047-0048]; once an index (which generates a score) can no longer be optimized, the process exits, indicating that when a goal score is achieved it will automatically be applied to the system before further processing).
Chishti et al. is silent to the digital jaw models being unsegmented.
Kitching et al. teaches an orthodontic treatment planning system in the same field of endeavor of methods for planning orthodontic treatment (abstract). Kitching teaches the method includes using computer models of the jaws (100 and 101) to simulate interactions among the teeth on the jaws. This allows the system to render realistic jaw movements for more accurate planning ([0029]). Furthermore, occlusion determination is considered for planning the treatment ([0029]). Kitching teaches segmented models may be used ([0061]). In some instances, use of an unsegmented digital representation may be desirable, rather than segmented as it avoids resource and/or labor-intensive processing steps to transform the unsegmented model to a segmented model ([0062]. Additionally, lower resolution or quality scans or images can save cost and time if the necessary reference points can be identified on the unsegmented scan or image ([0062]).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the invention to modify the method of Chishti et al. to use unsegmented jaw models, as taught by Kitching et al., as in some instances it may be unnecessary to segment the model to use it effectively and therefore that would avoid the need for additional processing steps and further save on time, resources and costs.
Chishti also does not explicitly teach wherein determining the score for each iterative bite position comprises accounting for any artifacts on the digital surface and their impact on bite setting.
Fisker et al. teaches a method in the same field of endeavor of simulating occlusion of teeth (abstract). Fisker teaches the method of planning the occlusion simulation are performed in an iterative manner ([0257]) and in some embodiments the same iterative performance is applied to modelling of appliances and for each change in the appliance, occlusion is simulated ([0261], note the appliances are the claimed artifacts).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the invention to modify the method of Chishti to include performing penetration fixing of the jaw in an iterative manner and considering an appliance to iteratively perform the occlusion simulation, as taught by Fisker et al., as it would consider the functional and dynamic occlusion situations and any additional variables such as orthodontic appliances to simulate a performance of the bite, yielding a more accurate and ideal occlusion.
Chishti teaches considering custom parameters and aims to evaluate deviation from normal alignment and occlusion, which is defined as all anatomical contact points being adjacent, with a good intercuspal mesh between upper and lower buccal teeth, and with nonexcessive overjet and overbite ([0047], ll. 5-12) and wherein penetration fixing iterations stop at a final position of the first digital jaw model with respect to the second digital jaw model if no cusp point moves below a predetermined threshold ([0066-0067]), but is silent to the movement threshold being more than 1 micron.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have the movement threshold between a cusp or the lower jaw and a cusp of the upper jaw be 1 micron as it would be an obvious matter of design choice as to achieve a normal or acceptable occlusal relationship between the teeth of the jaws. Note that applicant does not specify a criticality for the claimed value of 1 micron and therefore it would be obvious as such minimal valve indicates a small difference and hence would indicate that the condition has been met as it is very close to zero.
Chishti does not explicitly teach each cusp position is measured at the beginning and end of each penetration fixing iteration.
Imgrund teaches a method of determining occlusal distances using virtual models for assistance with treatment planning (see at least the abstract, [0013], [0019-0020]). Imgrund teaches a qualitative assessment of the occlusal contacts can be made to more completely assess the progress of treatment, and the current stage of occlusal contacts can be compared to the expected treatment outcome and initial occlusal contacts to Verify that the treatment is progressing properly ([0067]. Imgrund further teaches the cusp positions may be measured at two different points in time such that evaluation of before and after occlusal contacts can also be advantageous for bruxism analysis ([0076-0077]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method to include comparing cusp points at different and multiple time points (e.g., initially and later, beginning and end of a stage, etc.), as taught by Imgrund, because it would provide information that would aid with progress analysis, whether the conditions are met or not, occlusal relationship, and whether bruxism is occurring or not.
Regarding claim 2, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 1 (see rejection above). Chishti teaches wherein determining one or more iterative bite positions comprises determining a best transformation of one or more paired points at each iteration ([0050]). Chishti teaches that at step 346 of the optimization process, points between a previous and a current scan are matched to achieve a final position ([0050]).
Regarding claim 3, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 2 (see rejection above). Chishti teaches wherein the best transformation of one or more paired points at an iteration is used as the initial bite position in the next iteration ([0050]). The teachings of Chishti indicate that the “final position” of this stage is where the next stage will begin.
Regarding claim 4, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 2 (see rejection above). Chishti teaches wherein the one or more paired points comprises an attraction weighted pair ([0043] and [0047]). The points selected to represent a stage’s occlusion is determined based on meshing or good matching of two points between the upper jaw model and the lower jaw model.
Regarding claim 5, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 2 (see rejection above). Chishti teaches wherein the one or more paired points comprises interpenetration weighted pair ([0047]). Points with a good match are based on criteria including the two arches having non-excessive overjet and overbite (which would lead to interpenetration in the upper and lower models).
Regarding claim 7, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 1 (see rejection above). Chishti teaches wherein determining the rough bite approximation comprises determining an axial rough bite approximation ([0031]). Chishti teaches that initial arrangements are based on directions including an axial axis (106, Figure 2A).
Regarding claim 8, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 1 (see rejection above). Chishti teaches wherein determining rough bite approximation comprises a parabolic rough bite approximation ([0046-0047]). Chishti teaches that a best-fit technique may be used when optimizing jaw positions and wherein points are assigned to tooth cusps. Since a best fit line would connect the best points of a bite, the best-fit curve would take the shape of a parabola since the teeth of the jaw are arranged in a parabolic orientation.
Regarding claim 9, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 1 (see rejection above). Chishti teaches wherein determining one or more initial bite positions comprises performing forward direction shifts and side direction shifts from the rough bite approximation of the first digital jaw model ([0049]). Chishti teaches the method includes simulating a range of motions including lateral chewing movements and side to side movements as well as forward and backward.
Regarding claim 10, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 1 (see rejection above). Chishti teaches wherein determining the score comprises summing vertex scores from an extended tooth region, wherein each vertex score is a function of a signed distance from the other jaw ([0047]). Chishti teaches that each index represents the distance of a tooth from good occlusion (relative to the other jaw) and that the indices are summed up into one total score to represent the overall degree of deviation from a normal alignment and bite.
Regarding claim 11, Chishti in view of Kitching, Fisker and Imgrund teaches the method of claim 10 (see rejection above). Chishti teaches wherein the signed distance comprises positive values outside and negative values inside ([0047]). Chishti teaches that the distance and deviation is represented by a total score and that a good alignment should equal zero. The teachings of Chishti indicate that a positive score would represent a positive (forward) deviation and a negative score would represent a negative (backward) deviation due to the nature of a bite, and as those values are summed, the cancelling of the scores should result in zero to represent an absent deviation and ideal alignment.
Regarding claim 12, Chishti teaches a system for determining a bite setting (abstract), comprising:
a processor ([0012-0013]);
a computer-readable storage medium ([0012-0013]) comprising instructions executable by the processor to perform steps comprising:
receiving first and second digital jaw models (100, 101, figure 1);
determining a first digital jaw model center and a second digital jaw model center ([0058]; such that the midlines, which include centerpoints for each jaw are determined to be aligned);
determining a rough bite approximation of the first and second digital jaw models (step 202, Figure 3 and [0038]; “an initial data set (IDDS) representing an initial tooth arrangement is obtained” and being used as a starting position);
determining one or more initial bite positions of the first and second digital jaw models from the rough approximation (204, Figure 3 and [0038]; the IDDS is manipulated to produce a final digital data set (FDDS) corresponding to a desired tooth arrangement);
determining one or more iterative bite positions of the first and second digital jaw models for each of the one or more initial bite positions (206 Figure 3 and [0039]; both the IDDS and the FDDS are used to produce a plurality of intermediate digital data sets (INTDDs) to correspond to incrementally adjusted models);
determining one or more cusps on the first digital jaw model and one or more cusps on the second digital jaw model (see at least [0046], [0052-0059]; cusps of the teeth of the models are considered and used);
performing penetration fixing iterations to resolve jaw penetrations ([0040], [0046], [0066]; Chishti teaches iteratively generating subsequent data sets based on prior data sets until a final data set representing an acceptable tooth arrangement is achieved),
determining a score for each iterative bite position ([0047-0048]). Chishti teaches a process 300 is an optimization process that follows process 200. For optimization of the previously designed positions, an index is computed to identify the level of malocclusion and how far a tooth is from good occlusion at different bite representations (iterative bite positions). A score is assigned to various occlusal traits which make up a malocclusion. The scores of each position are summed to obtain an overall total, representing the degree a case deviates from normal alignment and occlusion. A score of zero indicates good alignment while a higher score indicates increased levels of irregularity ([0047]);
outputting the bite setting based on the score ([0047-0048]; once an index (which generates a score) can no longer be optimized, the process exits, indicating that when a goal score is achieved it will automatically be applied to the system before further processing).
Chishti et al. is silent to the digital jaw models being unsegmented. Kitching et al. teaches an orthodontic treatment planning system in the same field of endeavor of methods for planning orthodontic treatment (abstract). Kitching teaches the method includes using computer models of the jaws (100 and 101) to simulate interactions among the teeth on the jaws. This allows the system to render realistic jaw movements for more accurate planning ([0029]). Furthermore, occlusion determination is considered for planning the treatment ([0029]). Kitching teaches segmented models may be used ([0061]). In some instances, use of an unsegmented digital representation may be desirable, rather than segmented as it avoids resource and/or labor-intensive processing steps to transform the unsegmented model to a segmented model ([0062]. Additionally, lower resolution or quality scans or images can save cost and time if the necessary reference points can be identified on the unsegmented scan or image ([0062]).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the invention to modify the method of Chishti et al. to use unsegmented jaw models, as taught by Kitching et al., as in some instances it may be unnecessary to segment the model to use it effectively and therefore that would avoid the need for additional processing steps and further save on time, resources and costs.
Chishti also does not explicitly teach wherein determining the score for each iterative bite position comprises accounting for any artifacts on the digital surface and their impact on bite setting.
Fisker et al. teaches a method in the same field of endeavor of simulating occlusion of teeth (abstract). Fisker teaches the method of planning the occlusion simulation are performed in an iterative manner ([0257]) and in some embodiments the same iterative performance is applied to modelling of appliances and for each change in the appliance, occlusion is simulated ([0261], note the appliances are the claimed artifacts).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the invention to modify the method of Chishti to include performing penetration fixing of the jaw in an iterative manner and considering an appliance to iteratively perform the occlusion simulation, as taught by Fisker et al., as it would consider the functional and dynamic occlusion situations and any additional variables such as orthodontic appliances to simulate a performance of the bite, yielding a more accurate and ideal occlusion.
Chishti teaches considering custom parameters and aims to evaluate deviation from normal alignment and occlusion, which is defined as all anatomical contact points being adjacent, with a good intercuspal mesh between upper and lower buccal teeth, and with nonexcessive overjet and overbite ([0047], ll. 5-12) and wherein penetration fixing iterations stop at a final position of the first digital jaw model with respect to the second digital jaw model if no cusp point moves below a predetermined threshold ([0066-0067]), but is silent to the movement threshold being more than 1 micron.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have the movement threshold between a cusp or the lower jaw and a cusp of the upper jaw be 1 micron as it would be an obvious matter of design choice as to achieve a normal or acceptable occlusal relationship between the teeth of the jaws. Note that applicant does not specify a criticality for the claimed value of 1 micron and therefore it would be obvious as such minimal valve indicates a small difference and hence would indicate that the condition has been met as it is very close to zero.
Chishti does not explicitly teach each cusp position is measured at the beginning and end of each penetration fixing iteration.
Imgrund teaches a method of determining occlusal distances using virtual models for assistance with treatment planning (see at least the abstract, [0013], [0019-0020]). Imgrund teaches a qualitative assessment of the occlusal contacts can be made to more completely assess the progress of treatment, and the current stage of occlusal contacts can be compared to the expected treatment outcome and initial occlusal contacts to Verify that the treatment is progressing properly ([0067]. Imgrund further teaches the cusp positions may be measured at two different points in time such that evaluation of before and after occlusal contacts can also be advantageous for bruxism analysis ([0076-0077]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method to include comparing cusp points at different and multiple time points (e.g., initially and later, beginning and end of a stage, etc.), as taught by Imgrund, because it would provide information that would aid with progress analysis, whether the conditions are met or not, occlusal relationship, and whether bruxism is occurring or not.
Regarding claim 13, Chishti in view of Kitching, Fisker and Imgrund teaches the system of claim 12 (see rejection above). Chishti teaches wherein determining one or more iterative bite positions comprises determining a best transformation of one or more paired points at each iteration ([0050]). Chishti teaches that at step 346 of the optimization process, points between a previous and a current scan are matched to achieve a final position ([0050]).
Regarding claim 14, Chishti in view of Kitching, Fisker and Imgrund teaches the system of claim 13 (see rejection above). Chishti teaches wherein the best transformation of one or more paired points at an iteration is used as the initial bite position in the next iteration ([0050]). The teachings of Chishti indicate that the “final position” of this stage is where the next stage will begin.
Regarding claim 15, Chishti in view of Kitching, Fisker and Imgrund teaches the system of claim 13 (see rejection above). Chishti teaches wherein the one or more paired points comprises an attraction weighted pair ([0043], [0047]). The points selected to represent a stage’s occlusion is determined based on meshing or good matching of two points between the upper jaw model and the lower jaw model.
Regarding claim 16, Chishti in view of Kitching, Fisker and Imgrund teaches the system of claim 13 (see rejection above). Chishti teaches wherein the one or more paired points comprises interpenetration weighted pair ([0047]). Points with a good match are based on criteria including the two arches having non-excessive overjet and overbite (which would lead to interpenetration in the upper and lower models).
Regarding claim 18, Chishti in view of Kitching, Fisker and Imgrund teaches the system of claim 12 (see rejection above). Chishti teaches wherein determining the score comprises summing vertex scores from an extended tooth region, wherein each vertex score is a function of a signed distance from the other jaw ([0047]). Chishti teaches that each index represents the distance of a tooth from good occlusion (relative to the other jaw) and that the indices are summed up into one total score to represent the overall degree of deviation from a normal alignment and bite.
Regarding claim 19, Chishti teaches a non-transitory computer readable medium storing executable computer program instructions ([0012-0013]) for determining a bite setting ([0030]), the computer program instructions comprising instructions for:
receiving first and second digital jaw models (100, 101, figure 1);
determining a first digital jaw model center and a second digital jaw model center ([0058]; such that the midlines, which include centerpoints for each jaw are determined to be aligned);
determining a rough bite approximation of the first and second digital jaw models (step 202, Figure 3 and [0038]; “an initial data set (IDDS) representing an initial tooth arrangement is obtained” and being used as a starting position);
determining one or more initial bite positions of the first and second digital jaw models from the rough approximation (204, Figure 3 and [0038]; the IDDS is manipulated to produce a final digital data set (FDDS) corresponding to a desired tooth arrangement);
determining one or more iterative bite positions of the first and second digital jaw models for each of the one or more initial bite positions (206 Figure 3 and [0039]; both the IDDS and the FDDS are used to produce a plurality of intermediate digital data sets (INTDDs) to correspond to incrementally adjusted models);
determining one or more cusps on the first digital jaw model and one or more cusps on the second digital jaw model (see at least [0046], [0052-0059]; cusps of the teeth of the models are considered and used);
performing penetration fixing iterations to resolve jaw penetrations ([0040], [0046], [0066]; Chishti teaches iteratively generating subsequent data sets based on prior data sets until a final data set representing an acceptable tooth arrangement is achieved),
determining a score for each iterative bite position ([0047-0048]). Chishti teaches a process 300 is an optimization process that follows process 200. For optimization of the previously designed positions, an index is computed to identify the level of malocclusion and how far a tooth is from good occlusion at different bite representations (iterative bite positions). A score is assigned to various occlusal traits which make up a malocclusion. The scores of each position are summed to obtain an overall total, representing the degree a case deviates from normal alignment and occlusion. A score of zero indicates good alignment while a higher score indicates increased levels of irregularity ([0047]);
outputting the bite setting based on the score ([0047-0048]; once an index (which generates a score) can no longer be optimized, the process exits, indicating that when a goal score is achieved it will automatically be applied to the system before further processing).
Chishti et al. is silent to the digital jaw models being unsegmented. Kitching et al. teaches an orthodontic treatment planning system in the same field of endeavor of methods for planning orthodontic treatment (abstract). Kitching teaches the method includes using computer models of the jaws (100 and 101) to simulate interactions among the teeth on the jaws. This allows the system to render realistic jaw movements for more accurate planning ([0029]). Furthermore, occlusion determination is considered for planning the treatment ([0029]). Kitching teaches segmented models may be used ([0061]). In some instances, use of an unsegmented digital representation may be desirable, rather than segmented as it avoids resource and/or labor-intensive processing steps to transform the unsegmented model to a segmented model ([0062]. Additionally, lower resolution or quality scans or images can save cost and time if the necessary reference points can be identified on the unsegmented scan or image ([0062]).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the invention to modify the method of Chishti et al. to use unsegmented jaw models, as taught by Kitching et al., as in some instances it may be unnecessary to segment the model to use it effectively and therefore that would avoid the need for additional processing steps and further save on time, resources and costs.
Chishti also does not explicitly teach wherein determining the score for each iterative bite position comprises accounting for any artifacts on the digital surface and their impact on bite setting.
Fisker et al. teaches a method in the same field of endeavor of simulating occlusion of teeth (abstract). Fisker teaches the method of planning the occlusion simulation are performed in an iterative manner ([0257]) and in some embodiments the same iterative performance is applied to modelling of appliances and for each change in the appliance, occlusion is simulated ([0261], note the appliances are the claimed artifacts).
It would have been obvious for one having ordinary skill in the art before the effective filing date of the invention to modify the method of Chishti to include performing penetration fixing of the jaw in an iterative manner and considering an appliance to iteratively perform the occlusion simulation, as taught by Fisker et al., as it would consider the functional and dynamic occlusion situations and any additional variables such as orthodontic appliances to simulate a performance of the bite, yielding a more accurate and ideal occlusion.
Chishti teaches considering custom parameters and aims to evaluate deviation from normal alignment and occlusion, which is defined as all anatomical contact points being adjacent, with a good intercuspal mesh between upper and lower buccal teeth, and with nonexcessive overjet and overbite ([0047], ll. 5-12) and wherein penetration fixing iterations stop at a final position of the first digital jaw model with respect to the second digital jaw model if no cusp point moves below a predetermined threshold ([0066-0067]), but is silent to the movement threshold being more than 1 micron.
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to have the movement threshold between a cusp or the lower jaw and a cusp of the upper jaw be 1 micron as it would be an obvious matter of design choice as to achieve a normal or acceptable occlusal relationship between the teeth of the jaws. Note that applicant does not specify a criticality for the claimed value of 1 micron and therefore it would be obvious as such minimal valve indicates a small difference and hence would indicate that the condition has been met as it is very close to zero.
Chishti does not explicitly teach each cusp position is measured at the beginning and end of each penetration fixing iteration.
Imgrund teaches a method of determining occlusal distances using virtual models for assistance with treatment planning (see at least the abstract, [0013], [0019-0020]). Imgrund teaches a qualitative assessment of the occlusal contacts can be made to more completely assess the progress of treatment, and the current stage of occlusal contacts can be compared to the expected treatment outcome and initial occlusal contacts to Verify that the treatment is progressing properly ([0067]. Imgrund further teaches the cusp positions may be measured at two different points in time such that evaluation of before and after occlusal contacts can also be advantageous for bruxism analysis ([0076-0077]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the invention to modify the method to include comparing cusp points at different and multiple time points (e.g., initially and later, beginning and end of a stage, etc.), as taught by Imgrund, because it would provide information that would aid with progress analysis, whether the conditions are met or not, occlusal relationship, and whether bruxism is occurring or not.
Response to Arguments
Applicant's arguments filed 4/14/2026 have been fully considered but they are not persuasive.
Rejections Under 35 U.S.C. 101
Applicant argues that the amended claim includes steps that cannot practically be performed in the human mind and therefore do not recite a mental process and even if the claims recite an abstract idea, additional elements integrate the judicial exception into practical application.
However, the amended limitations, include steps that could be done by a generic computer or imagined in the human mind. Although inconvenient, a person can look at a patient’s mouth or a physical model of it and determine an offset point between two jaws and calculate an overlap between them and stop once a threshold value is reached and so it is an abstract idea that adds nothing significantly more to the claim to take it out of being a judicial exception. The amended limitations including “determining a first unsegmented digital jaw model center and a second unsegmented digital jaw model” and “wherein each cusp point position is measured at the beginning and end of each penetration fixing iteration” are still considered steps that can be done in the human mind or by hand such that one can identify a center of a jaw model and repeating the calculation at different time points to determine whether to stop once a threshold value is reached.
The amended claim minorly changes the intangible data (i.e., the digital jaw models) that is considered in the processes. Examiner notes that even though the invention is understood in light of the specification and drawings, the specification is not read into the claim when being examined and rejected. Therefore, there are no further elements in the claim that amount to significantly more than the judicial exception (abstract idea). The method as disclosed is performed on a generic use computer.
Under Step 2A, Prong 1, the claims are directed to an abstract idea such that the steps involve data gathering and data analysis.
Under Step 2A, Prong 2, the claims fail to integrate the abstract idea into a practical application. The method merely uses a computing system to perform the abstract idea.
Under Step 2B, the claims do not recite an inventive concept sufficient to transform the abstract idea into patent-eligible subject matter. The additional elements involve mere data gathering (i.e., receiving models), and conventional computing steps that a generic computer can perform. The method being computer-implemented and that step of outputting the bite, are considered a mere invocation of a generic computer and a generic result output step that would not amount to significantly more.
Therefore claim 1 is not eligible subject matter under 35 USC 101.
Prior art rejections:
Applicant's arguments filed 4/14/2026 have been fully considered but they are not persuasive.
Applicant argues that the prior art not teach the amended limitation of “determining a first unsgmented digital model center and a second unsegmented model center”. However, Chishti teaches aligning midlines of the arches ([0058]). However, although Chishti does not teach the models being unsegmented, Kitching contemplates both the use of segmented or unsegmented models. Kitching’s teachings of using unsegmented models for accurate planning provides the advantages of avoiding resources and/or labor-intensive processing steps (Kitching paragraph 62) and hence one of ordinary skill in the art would be motivated to combine the two as they both use teeth models for orthodontic planning and the modification would provide said additional advantages to the method. Therefore, the combination meets the limitations of the amended claim.
With respect to the other limitations, as clarified, note the new grounds of rejection teaching analyzing cusp position at different time points.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892 attached to this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LINA FARAJ whose telephone number is (571)272-4580. The examiner can normally be reached Monday-Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Edelmira Bosques can be reached at (571) 270-5614. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/LINA FARAJ/ Examiner, Art Unit 3772
/HEIDI M EIDE/ Primary Examiner, Art Unit 3772
8/18/2026
Prosecution Insights