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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-5, 7-16, 18-19 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 12 and 19 recite the limitation “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 more than 1 micron”. It is unclear what is meant by “if no cusp moves more than 1 micron” such that it is unclear what exactly is the movement representing. It is unclear whether the cusp movement distance is a distance between an upper cusp and a lower cusp or whether it is referring to a minor change between a cusp on a previous model and a cusp of a current model (i.e., a minor subsequent change in the individual position of each cusp). For examining purposes, it was understood that the iterations stop when a distance between an upper cusp and a lower cusp of the digital models reaches 1 micron.
Claims 1, 12, 19 recite the limitation “measured at the beginning and end of each penetration fixing iteration”. It is unclear whether measuring is done as a checkpoint (i.e., checking if the cusp point moves more than 1 micron) before the next iteration and therefore automatically happens to be at the end of one iteration and the beginning of a next iteration or whether the measuring is repeated due to a period of time between iterations. According to paragraph 87 of the specification, the measuring is done at the beginning and end of each penetration fixing iteration to determined change. Assuming that the “change” is the 1 micron threshold value, it was understood for examining purposes, that after an iteration, a measurement step takes place to measure the change in distance of a cusp point to check whether it moved more than 1 micron or not and therefore is automatically between two iterations.
Claims 2-5, 7-11, 13-16, 18 are rejected under 35 USC 112(b) by virtue of dependency.
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 Fisker et al. (US 2013/0066598 A1), and further in view of Kitching et al. (US 2008/0306724 A1).
Regarding claim 1, Chishti et al. teaches a computer-implemented method (abstract) of determining a bite setting ([0030], “determining the occlusion”), comprising:
receiving first and second digital jaw models (100, 101, figure 1);
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.”);
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 ([0046]; cusps of the teeth of the models are considered and used);
performing penetration fixing iterations to resolve jaw penetrations ([0040]; 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]). 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]; 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 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 dimensions are proper for relationships between the teeth. It is further obvious that the measuring/checking of the threshold is done between iterations, such as end of one and the beginning of another, since the iterations are intended to stop if the threshold value is reached and therefore would require determination of whether the threshold is reached before a next iteration begins.
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.
Regarding claim 2, Chishti in view of Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 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.”);
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 ([0046]; cusps of the teeth of the models are considered and used);
performing penetration fixing iterations to resolve jaw penetrations ([0040]; 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]). 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]; 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 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 cusp 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 is further obvious that the measuring/checking of the threshold is done between iterations, such as at the end of one and at the beginning of the next, since the iterations are intended to stop if the threshold value is reached and therefore would require determination of whether the threshold is reached before a next iteration begins.
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 dimensions are proper for relationships between the teeth.
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.
Regarding claim 13, Chishti in view of Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 Fisker and Kitching 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 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.”);
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 ([0046]; cusps of the teeth of the models are considered and used);
performing penetration fixing iterations to resolve jaw penetrations ([0040]; 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]). 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]; 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 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 cusp 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 is further obvious that the measuring/checking of the threshold is done between iterations, such as at the end of one and at the beginning of the next, since the iterations are intended to stop if the threshold value is reached and therefore would require determination of whether the threshold is reached before a next iteration begins.
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 dimensions are proper for relationships between the teeth.
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.
Response to Arguments
Applicant's arguments filed 6/10/2024 have been fully considered but they are not persuasive.
Rejections Under 35 U.S.C. 112
Applicant argues that the amended claims clarify the claims and overcome the rejection. However, the claims are amended to say “penetration fixing iterations stop at a final relative position of the first digital jaw model with respect to the second digital jaw model if no cusp point moves more than 1 micron measured at the beginning and end of each penetration fixing iteration”. This does not clarify the claim as it is still unclear what movement is being measured exactly. It is unclear whether the measured value is a difference between a position of a cusp of one tooth of the first digital model in comparison to the cusp of the one tooth of the second digital model or whether the measured value is a distance of overlap/penetration of an upper cusp and a lower cusp of the first digital model when compared to the second digital model. It is now also unclear how often the measuring is done and whether it is one measurement between each iteration (which happens to be after one iteration and therefore before the next) or whether the measurement is repeated before the next iteration due to a period of time in between iterations. Further clarification is required.
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. Applicant adds that the amended claims achieve an improved technological result that integrates any alleged judicial exception into practical application and that the amended limitations together provide significantly more than the alleged abstract ineligible matter. Applicant argues that the amended limitations achieve an improved technological result and therefore integrate any alleged judicial exceptions into a practical application technical improvement and cites paragraph 97 of the specification which discusses advantages of full automation of the steps and the advantage of not requiring preprocessing because of the use of unsegmented models.
However, the amended limitations, even if they’re providing a technical improvement, 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 one or more cusps on the first unsegmented digital jaw model and one or more cusps on the second unsegmented digital jaw model” and “wherein penetration fixing iterations stop at a final relative position of the first digital jaw model with respect to the second digital jaw model if no cusp point moves more than 1 micron measure 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 cusps of a tooth model and repeating the calculation to know when to stop once a threshold value is reached.
All of the claimed steps including:
• 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
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.
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. Therefore claim 1 is not eligible subject matter under 35 USC 101.
Applicant cites paragraph 98 of the specification disclosing that the method can determine bite alignment using one or more steps even if there are artifacts on the digital surface that can impede bite settings and argues that the claims include significantly more than the alleged judicial exception. However, this is not persuasive because one can consider artifacts on a physical model or articulator to determine the occlusal relationship as well. Applicant does not provide any evidence or expert testimony as to how the claims recite additional elements that amount to significantly more than the judicial exception. The only additional elements in the claims are that the method is computer-implemented and that the step of outputting the bite, which are a mere invocation of a generic computer and a generic result output step that would not amount to significantly more.
Prior art rejections:
Applicant's arguments filed 8/18/2025 have been fully considered but they are not persuasive.
Applicant argues that Chishti does not disclose or suggest performing penetration fixing iterations to resolve jaw penetrations as claimed and that one of ordinary skill in the art would not be motivated to combine Chishti and Fiscker to teach the amended claim. Chishti teaches using selected pointed on adjacent teeth and iterating until acceptable results are achieved and the system stops when relative positions of the teeth satisfy a predetermined target ([0040-0041]). Chishti further teaches optimizing occlusion and based on that determination finalizing the positions of the teeth and using relationship between mandibular and maxillary cusps to idealize the patients bite ([0030], [0039], [0047], [0051]-[0060]). Chishti further discloses using collision detection ([0043]) and considers overlap of maxillary and mandibular teeth ([0058]). Fiscker teaches a method of simulating occlusion and in some embodiments, considering appliances and occlusion being simulated for each appliance ([0261]). Therefore, the method of Chishti and Fiscker teaches using iterative analysis to idealize occlusal contacts and collisions, including the steps of determining a score, comparing positions between models, repeating iterations and stopping when a preset value is reached. Chishti/Fiscker teaches consideration of artifacts, such as appliances, in the occlusal determination. Please note that the claimed limitation “performing penetration fixing iterations to resolve jaw penetrations”, according to its broadest reasonable interpretation, is a step of iterating overlap of upper and lower teeth to achieve an ideal occlusion.
Applicant further argues that Chishti is directed to systems and methods for positioning teeth and uses a differential distance between each tooth and its neighbors is used as an index to move the teeth discloses the claimed limitations specifically to resolve jaw penetrations, determine a score for each bite position and measuring at the beginning and end of each penetration fixing iteration to determine if the first jaw model with respect to the second jaw model has a cusp point that moves more than 1 micron. Chishti teaches optimization of occlusion and based on the determined ideal relationship, achieving the desired tooth positions ([0030], [0039], [0047], [0051]-[0060]) and therefore all of the steps can be applied to an upper tooth and its adjacent lower tooth and calculating their relationship until a satisfactory occlusion is achieved. Although Chishti does not teach the specific claimed value of movement between cusps of the models being 1 micron, it would have been obvious to one having ordinary skill in the art that the change between the cusps should be minimal for a proper occlusion.
Applicant also argues that one would not be motivated to combine Chishti and Kitching to arrive at the claimed limitations. However, although Chishti does not teach the models being unsegmented, 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.
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.
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.
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/LINA FARAJ/ Examiner, Art Unit 3772
/HEIDI M EIDE/ Primary Examiner, Art Unit 3772
11/12/2025