Patent Grant
11096763
All publications and patent applications mentioned in this specification are herein incorporated by reference in their entirety to the same extent as if each individual publication or patent application was specifically and individually indicated to be incorporated by reference.
Treatment planning for orthodontic treatment using a series of patient-removable appliances to reposition the teeth.
Orthodontic and dental treatments using a series of patient-removable appliances (e.g., “aligners”) are very useful for treating patients, and in particular for treating malocclusions. Treatment planning is typically performed in conjunction with the dental professional (e.g., dentist, orthodontist, dental technician, etc.), by generating a model of the patient's teeth in a final configuration and then breaking the treatment plan into a number of intermediate stages (steps) corresponding to individual appliances that are worn sequentially. This process may be interactive, adjusting the staging and in some cases the final target position, based on constraints on the movement of the teeth and the dental professional's preferences. Once the final treatment plan is finalized, the series of aligners may be manufactured corresponding to the treatment planning.
This treatment planning process may include many manual steps that are complex and may require a high level of knowledge of orthodontic norms. Further, because the steps are performed in series, the process may require a substantial amount of time. Manual steps may include preparation of the model for digital planning, reviewing and modifying proposed treatment plans (including staging) and aligner features placement (which includes features placed either on a tooth or on an aligner itself). These steps may be performed before providing an initial treatment plan to a dental professional, who may then modify the plan further and send it back for additional processing to adjust the treatment plan, repeating (iterating) this process until a final treatment plan is completed and then provided to the patient.
The methods and apparatuses described herein may improve treatment planning, including potentially increasing the speed at which treatment plans may be completed, as well as providing greater choices and control to the dental professional, and allowing improved patient involvement in the treatment planning process.
Described herein are orthodontic and/or dental treatment planning methods and apparatuses. In particular, described herein are methods of planning a dental and/or orthodontic treatment. Any of the methods and apparatuses described herein may pre-calculate a plurality of potential treatment plan variations including all of the staging and various, potentially alternative, final configurations and display them in parallel. A very large number of such plans may be generated at once (e.g., as a large set or array) quickly and reviewed in real time or near real-time. A treatment plan may include a plurality of different stages during which the patient's teeth are moved from an initial position to a final position; a dental aligner (e.g., a shell aligner) or other orthodontic device may be made to correspond to each stage, and worn in the sequence defined by the treatment plan to move the patients teeth from their initial position to a final position.
The methods and apparatuses described herein allow for the concurrent generation of a large number of treatment plan variations in which each variation is optimized to best address the dental professional's (and in some cases, the patient's) treatment goals, as well as approximating as closely as possible an ideal or target final position. Also described herein are orthodontic and/or dental treatment planning methods and apparatuses that allow a dental professional (e.g., a “user”) and/or a patient to form, modify, and select a treatment plan from a plurality of different treatment plans, in real time.
For example, a dental professional may make a model (e.g., a digital model or scan, and/or a physical model, which may subsequently be digitized) and send it to a remote site (e.g., a laboratory) where multiple options for treatment plans may be generated. The model may be transmitted along with one or more of: treatment preferences from the dental professional specific to the patient, treatment preferences specific for the particular dental professional that may be applied to all patient's associated with that dental professional, and/or an indication of what clinical product(s) (e.g., orthodontic product) should be used to move the patient's teeth. This data may be used as inputs to generate the plurality of optional treatment plans, will be described in greater detail below. The resulting multiple treatment plans (which may collectively be referred to as an array of treatment plans, a set of treatment plans, or a collection of treatment plans or treatment plan variations) may then be transmitted back to the dental professional for interactive display, selection and/or modification by the dental professional and/or patient. Note that each treatment plan may have multiple stages, wherein at each stage of that treatment plan an aligner may be worn for a predetermined period of time; alternatively or additionally in some variations one or more stage may include orthodontic/dental manipulations (e.g., tooth removal, interproximal reduction, etc.) on the patient's teeth.
The methods and apparatuses described herein allow the rapid and creation of a large number of full treatment plans specific and customized to a patient setting froth an orthodontic and/or dental plan for beneficially modifying the subject's dentition, including in particular, moving (e.g., aligning, straightening, etc.) the patients teeth and/or resolving orthodontic issues specific to the patient. Traditionally, only a single orthodontic treatment plan was provided to a user and/or patient, in which the patient's dentition was modified from the patients initial dental position to a final dental position, often a comprehensive final position of the patient's teeth. In general a treatment plan may include a series of patient-removable appliances to reposition the teeth, and some indication of the duration of time each appliance (“aligner”) is to be worn. At each stage of the treatment plan, one or more (or all) of the patient's teeth maybe moved relative to the prior stage, until the teeth are in a target configuration; once in the target configuration, the final stage(s) may optionally be a retainer (or multiple retainers) to maintain the target configuration for some retaining duration. Any number of stages may be used. In some variations the treatment plan may also indicate one or more dental/orthodontic procedures to be performed at that stage (e.g., interproximal reduction, tooth extraction, etc.).
The apparatuses (e.g., systems, devices, etc. including software, firmware and hardware), which may include treatment plan solvers, and methods described herein may rapidly generate a plurality of different treatment plans, each customized to the patient. These method and apparatuses may take into account the treatment preferences of the dental professional and/or patient, and/or the types and constraints of available appliances (aligners) when generating these treatment plans. Alternatively plans in which one or more of these parameters are different may be generated, allowing direct comparison between a large number of alternative plans. Typically generating even a single treatment plan has proven complex and time intensive. Manual techniques have been used for treatment planning and may require many hours or days to complete. Automation has also proven difficult, particularly when estimating or preventing collisions between teeth during the treatment. The methods and apparatuses described herein may provide an extremely fast and effective way to generate (and in some cases generate concurrently or nearly concurrently) a large number of different and therapeutically viable treatment plans. Treatment plans may be generated with no or minimal technician or dental professional oversight required. In some variations of the methods and apparatuses described herein more than 3 (e.g., more than 4, 5, 6, 7, 8, 9, 10, 12, 14, 16, 18, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 125, 150, 175, 200, 250, 300, 350, 400, 500, etc.) complete treatment plans, each including a variety of stages and corresponding tooth position and/or appliance (e.g., aligner) configuration (in some variations the tooth position at each stage may be used to generate a dental appliance).
For example, described herein are automated methods of creating a plurality of dental/orthodontic treatment plans, as well as devices for performing them (e.g., treatment plan solvers). An automated method of creating a plurality of variations of treatment plans to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages may include: (a) specifying a set of treatment preferences and a set of treatment details (the treatment preferences and treatment details may be automatically or manually specified); (b) automatically determining a treatment plan based on the specified treatment preferences and treatment details, by: collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), in a processor: a digital model of a patient's teeth, and accessing, by the processor, the set of treatment preferences, a comprehensive final position of the patient's teeth, and the set of treatment details; selecting a plurality of numerically expressed treatment targets from a memory accessible to the processor, based on the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth; combining the plurality of numerically expressed treatment targets to form a single numerical function; selecting a plurality of numeric limits on the single numerical function based on the set treatment preferences; minimizing the single numerical function subject to the plurality of numeric limits to get a solution vector including all stages forming the treatment plan; and mapping the solution vector to a treatment plan, wherein the treatment plan includes a final tooth position that is different from the comprehensive final position of the patient's teeth (c) adding the treatment plan to an array of treatment plans; and (d) modifying one or more of the treatment details or treatment preferences and repeating steps (b)-(d) at least once. The method may also include adding metadata identifying the treatment details or treatment preferences to each treatment plan so that the array of treatment plans includes the identifying metadata. The metadata does not need to indicate all of the treatment details or treatment preference information, but may include just a subset of it, such as just those parameters that are different from the other treatment plans (e.g., number of stages, use of IPR, etc.).
In any of the methods and apparatuses described herein, minimizing the single numerical function subject to the plurality of numeric limits to get a solution vector including all stages forming the treatment plan may include estimating collisions between adjacent teeth. Thus, any of these apparatuses may include a collision detector, and particular a collision detector that determines the magnitude and/or velocity of collisions (or separation, which is negative collision) between teeth. The use of very rapid collision detection/detectors may enhance the rate and efficiency of the methods and apparatuses for treatment planning described herein; automated collision detection methods and systems (collision detectors) are also described in greater detail herein.
In some variations the method may also include an automated analysis of the final stage tooth position to determine the amount or level of correction of the patient's malocclusions achieved. This treatment outcome information may be added, e.g., as metadata, to the array of treatment plans.
Also described herein are methods and apparatuses for generating or translating user-specific treatment preferences. Dental professionals (e.g., users) may provide comments, requests and feedback on treatment plans across many cases. The methods and apparatuses described herein may interpret these comments, request and feedback, as treatment preferences. For example, these treatment preferences may include preferences with respect to modifying the patient's teeth (e.g., IPR, including which teeth to perform IPR on, what stage of treatment to perform IPR, etc.), the use of attachments (to use/not to use, where to place them, when to use them), and the like. The methods and apparatuses described herein may build and use a database of such user-specific treatment preferences. This database may be updated and modified as the user performs additional cases. Further, this database may be accessed to generate a treatment plan (or an array of treatment plans), as described herein.
For example, an automated and customized method for creating an orthodontic treatment plan of a patient's teeth for a specified dental professional may include: collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), in a treatment plan optimizing engine (e.g., a processor), a set of combined treatment preferences specific to the specified dental professional, wherein the set of combined treatment preferences comprises a first set of rules converted from a set of textual instructions from the specified dental professional into a domain-specific language specific to the specified dental professional, further wherein the textual instructions comprise unscripted instructions, and a second set of rules converted from a set of scripted instructions from the specified dental professional, wherein the scripted instructions comprise responses from a script of predefined choices; receiving, in the treatment plan optimizing engine, a digital model of the patient's teeth; and generating, with the treatment plan optimizing engine, a treatment plan for the patient's teeth using the set of combined treatment preferences and the digital model of the patient's teeth. The different treatment properties may comprise one or more of: interproximal reduction (IRR), extraction, and aligner attachments.
The scripted instructions from the specified dental professional may be specific to the patient's teeth. The textual instructions may be specific to the patient's teeth, and/or the textual instructions may be extracted from a plurality of different prior cases by the specified dental professional.
The textual instructions may comprise instructions on one or more of, for example, staging of interproximal reduction and positions of attachments. Any other treatment preference may be included as a textural instruction.
In general, any of the methods may include updating the domain-specific language specific to the specified dental professional, and/or storing the domain-specific language specific to the specified dental professional in a remote database accessible by the treatment plan optimizing engine. Any of these methods may include automatically generating the domain-specific language specific to the specified dental professional, or manually converting textual instructions into the domain-specific language specific to the specified dental professional.
Any of these methods may include identifying the specified dental professional to the treatment plan optimizing engine; for example the dental professional may be identified by an identifier (dental professional identifier) such as name (last, first, etc.), practice name, number, etc. The identifiers described herein may uniquely identify the dental professional.
As described in greater detail herein, typically generating the treatment plan comprises determining a final position of the patient's teeth, and determining each of a plurality of stages of tooth movement based on the combined treatment preferences, wherein each stage comprises a dental aligner.
For example, described herein are automated and customized method for creating an orthodontic treatment plan of a patient's teeth for a specified dental professional, the method comprising: receiving, in a processor, a set of scripted instructions from a specified dental professional, wherein the scripted instructions comprise responses from a script of predefined choices; receiving in the processor, a set of textual instructions from the specified dental professional, wherein the textual instructions comprise unscripted instructions; converting the set of textual instructions into a domain-specific language specific to the specified dental professional, wherein the domain-specific language comprises a first set of rules; converting the set of scripted instructions into a second set of rules; combining the first set or rules and the second set of rules into a set of combined treatment preferences; and passing the set of combined treatment preferences to a treatment plan optimizing engine, wherein the treatment plan optimizing engine generates a treatment plan using the combined treatment preferences.
Any of these methods may also include accessing, by the processor, a database of the domain-specific language corresponding to the specified dental professional.
Any of the methods described herein may be performed by an apparatus configured and/or adapted to perform them. For example, also described herein are non-transitory computer-readable storage media having stored thereon, computer-executable instructions that, when executed by a computer, cause the computer to automatically create an orthodontic treatment plan of a patient's teeth customized for a specified dental professional by: receiving, in a treatment plan optimizing engine, a set of combined treatment preferences specific to the specified dental professional, wherein the set of combined treatment preferences comprises a first set of rules converted from a set of textual instructions from the specified dental professional into a domain-specific language specific to the specified dental professional, further wherein the textual instructions comprise unscripted instructions, and a second set of rules converted from a set of scripted instructions from the specified dental professional, wherein the scripted instructions comprise responses from a script of predefined choices; receiving, in the treatment plan optimizing engine, a digital model of the patient's teeth; and generating, with the treatment plan optimizing engine, a treatment plan for the patient's teeth using the set of combined treatment preferences and the digital model of the patient's teeth.
As mentioned above, also described herein are methods and apparatuses (e.g., systems, devices, etc., including software) for automatically detecting and/or estimating collisions between teeth. These methods and apparatuses may provide an approximation of the magnitude and/or velocity of collisions between teeth. The magnitude and velocity of a collision may be expressed as a vector or a scalar value or values. The velocity may be velocity in each of a plurality of axes, such as three translational axes (x, y, z) and three rotational axes (pitch, roll, yaw). In some variations the magnitude and velocity of collisions may be determined between all adjacent pairs of teeth. The magnitude of a collision may be positive (e.g., indicating the depth of collision) or negative (indicating separation or spacing between teeth).
Although the automated collision detection systems and methods described herein may be particularly useful as part of automated method for generating treatment plans (e.g., as part of a treatment plan solver), they are not limited to this. It should be understood that the methods and apparatuses described herein may also be used independently, or as part of any other method or apparatus that may benefit from automatic and rapid detection of collision between two or more teeth.
For example, a method of automatically determining collisions between adjacent teeth may include: forming a digital model of a surface for each tooth of a plurality of a patient's teeth by automatically packing a plurality of 3D shapes to approximate the surface for each tooth, wherein the 3D shapes each have a core that is a line segment or a closed plane figure and an outer surface that is a constant radius from the core; forming, for the digital model of the surface for each tooth, a hierarchy of bounding boxes enclosing the plurality of 3D shapes; measuring, in the collisions detector, a magnitude and a velocity of a collision between adjacent teeth when the plurality of the patient's teeth are in a set of tooth positions by identifying colliding bounding boxes and determining the spacing or overlap between the 3D shapes bound by the colliding bounding boxes; and outputting the magnitude and velocity of the collision between adjacent teeth corresponding to the set of tooth positions.
Any of the methods for automatically determining collisions may also include collection (e.g., receiving, downloading, accessing, etc.) a set of tooth positions. The tooth positions may correspond to the positions of the teeth in space. In some variations the teeth may be arranged in a coordinate system (e.g., x, y, z) allowing relative positions of the teeth to be understood.
In general, in any of the methods and apparatuses for automatically determining collisions (e.g., collision detectors) described herein, one or more surfaces of the patient's teeth may be filled with the 3D shapes. As used herein a 3D shape may refer to a shape having a core that is a line or planar (including closed planar) figure, and an outer 3D surface surrounding and extending from the core by a set radius, r. In some variations the 3D shapes are capsules, which have a core that is a line segment extending some length, L. The body of the 3D shape (capsule body) is elongated but rounded at the end, having semi-spherical ends. Any appropriate 3D shape, or combination of 3D shapes may be used. These 3D shapes may exclude spheres, which typically have a point at the core, surrounded by a surface at an outer radius. 3D shapes having a core that is a line or a plane figure, and particularly closed plane figures (such as rectangles, circles, etc.) that form shapes having both linear and convex outer surfaces may work much better than simple spheres when filling tooth surfaces, which may be concave, convex, as well as flat in some regions.
In general, when automatically determining collision between two teeth, each of the two teeth being processed may be divided into portions, so that just those surfaces of the portions of the teeth that face each other need to be filled/modeled with the 3D shapes (e.g., capsules). In some variations the teeth may be first divided in half, into a left half and a right half, and only the sides of the teeth that face each other need to be processed. Thus, forming the digital model of the surface for each tooth may include separately forming a left side and a right side of each tooth. Thus, measuring the magnitude and velocity of the collision between adjacent teeth may comprise identifying collisions between adjacent left sides and right sides of the adjacent teeth.
In any of the collision detection methods and apparatuses described herein, forming the digital surface of the surface for each tooth may include automatically packing the plurality of 3D shapes to approximate the surface. In some the surface (and/or the entire tooth volume, or a portion of the tooth volume, such as the left side/right side) may be modeled by packing with a plurality of 3D shapes (e.g., capsules) that are all the same shape and size, or different shapes and/or different size 3D shapes may be used. In processing to determine collisions (and/or spacing) between adjacent teeth, the closet capsules between each of the two teeth may be determined by bounding boxes and one or more numeric methods may be used to solve for the collision magnitude (which may be overlap/collision and be a positive magnitude value or may be spacing and be a negative collision magnitude).
Thus, and of the methods and apparatuses for collision detection described herein may also include forming a hierarchy of bounding boxes around the 3D shapes (e.g., capsules) forming the surface of the first tooth and a second hierarchy of bounding boxes may be determined around the 3D shapes (e.g., capsules) forming the surface of the second tooth that is adjacent to the first tooth. The use of a tiered hierarchy of bounding boxes around the 3D shapes forming the surfaces allows for a computationally rapid determination of which of the bounding boxes in the hierarchy do and do not overlap/collide. At the lowest tier of the hierarchy each bounding box encloses a single 3D shape; each subsequent tier bound two adjacent bounding boxes, until the highest tier, which bounds all of the bounding boxes around all of the 3D shapes. Forming the hierarchy of bounding boxes may comprises, for example, forming a hierarchy in which bounding boxes of a lowest tier of the hierarchy each enclose a single 3D shape, subsequent tiers of the hierarchy each enclose adjacent bounding boxes or groups of bounding boxes, and a highest tier of the hierarchy encloses all of the bounding boxes around the 3D shapes forming the surface. When comparing the hierarchy of bounding boxes, the comparison may start at the top of the hierarchy; if the top-tier hierarchy for each adjacent tooth surface does not collide, then none of the bounding boxes in the hierarchy will collide. Each branch of the first hierarchy (e.g., corresponding to the first tooth surface) may be compared with each branch of the second hierarchy (e.g., corresponding to the second tooth surface) starting from the top tier and working down the hierarchy tiers; at any tier that there is no overlap (e.g., collision, which may be detected by measuring the shortest distance between the bounding boxes at that tier) between two branches, no further lower-tiered branches need to be examined. Once the lowest tier has been reached, the 3D shapes in the colliding bounding boxes may then be used to determine the collision magnitude and velocity.
The magnitude of the collision between adjacent teeth may be measured by, for example, determining the spacing or overlap between the 3D shapes bound by the colliding bounding boxes to get the magnitude of the collision. As just mentioned, measuring the magnitude and velocity of the collision between adjacent teeth may include starting from a top tier of the hierarchy of bounding boxes and proceeding down the tiers to the lowest tier of the hierarchy of bounding boxes corresponding to each adjacent tooth, comparing the branches at each tier between the two adjacent teeth in order to identify colliding bounding boxes at the lowest tier of the hierarchy of bounding boxes. Once the colliding bounding boxes are determined, the shortest distance (the maximum overlap) between the two 3D shapes (e.g., capsules) may be computationally determined.
The velocity of the collision between adjacent teeth may be measured by ‘jittering’ one or both of the colliding teeth relative to the other; for example, one of the 3D shape-filled teeth (or portion of the tooth) may be moved very slightly (e.g., between 0.001-0.0001 inch) in each of one of the spatial axes (e.g., x, y, z, pitch, roll, yaw) and determining the change in the collision after each sequential movement. For example, by sequentially adjusting a position of a first 3D shape of the adjacent teeth by a small predetermined amount in each of a plurality of axes of the first tooth and determining the change in spacing or overlap between the 3D shapes bound by the bounding boxes to get the velocity of the collision for each of the corresponding axes. The change in collision/spacing magnitude following each change may be expressed for that axes as the velocity (the time to achieve the change may be assumed to be a set value, e.g., 1).
The velocity may be expressed as a single vector (e.g., the x, y, z direction) and/or rotation; in some variations the velocity may be expressed in each of the axes (three spatial and 3 rotational). Alternatively, in some variations, only the three spatial (or three rotational) axes are examined. The resulting collision magnitude and velocity may be output, e.g., by outputting the magnitude and the velocity for each of three spatial and three rotational axes. The output (magnitude, velocity or both magnitude and velocity) may be passed to the treatment planning solver, or to any other system that the collision detector is part of, and/or it may be output (e.g., displayed, stored, transmitted, etc.) for independent use.
Also described herein are systems for automatically detecting collisions between teeth. These systems may be referred to as collision detectors and may include, for example: one or more processors; and a memory coupled to the one or more processors, the memory configured to store computer-program instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising: forming a digital model of a surface for each tooth of a plurality of a patient's teeth by automatically packing a plurality of 3D shapes to approximate the surface for each tooth, wherein the 3D shapes each have a core that is a line segment or a closed plane figure and an outer surface that is a constant radius from the core; forming, for the digital model of the surface for each tooth, a hierarchy of bounding boxes enclosing the plurality of 3D shapes; measuring, in the collisions detector, a magnitude and a velocity of a collision between adjacent teeth when the plurality of the patient's teeth are in a set of tooth positions by identifying colliding bounding boxes and determining the spacing or overlap between the 3D shapes bound by the colliding bounding boxes; and outputting the magnitude and velocity of the collision between adjacent teeth corresponding to the set of tooth positions.
As mentioned above, any of these method of detecting collisions and/or collision detectors may be included as part of a system or method for automatically creating an orthodontic treatment plan of a patient. For example, a method of automatically creating an orthodontic treatment plan for a patient may include: forming a digital model of a surface for each tooth of a plurality of a patient's teeth by packing a plurality of 3D shapes to approximate the surface for each tooth, wherein the 3D shapes each have a core that is a line segment or a closed plane figure and an outer surface that is a constant radius from the core; forming, for the surface for each tooth of the plurality of the patient's teeth, a hierarchy of bounding boxes enclosing the plurality of 3D shapes; passing a set of tooth positions for the plurality of the patient's teeth from a treatment plan solver to a collision detector; measuring, in the collisions detector, a magnitude and a velocity of a collision between adjacent teeth when the plurality of the patient's teeth are in the set of tooth positions by identifying colliding bounding boxes and determining the spacing or overlap between the 3D shapes bound by the colliding bounding boxes; and passing the magnitude and velocity of the collision between adjacent teeth corresponding to the set of tooth positions to the treatment plan solver; and outputting, from the treatment plan solver, one or more orthodontic treatment plans comprising a series of dental appliances for moving the patient's teeth.
A method of automatically creating an orthodontic treatment plan of a patient may include: collecting a digital model of a plurality of the patient's teeth, wherein each tooth of the digital model is segmented; automatically packing a plurality of 3D shapes to approximate a surface for each tooth of the digital model, wherein the 3D shapes each have a core that is a line segment or a closed plane figure and an outer surface that is a constant radius from the core; forming, for the surface for each tooth of the digital model, a hierarchy of bounding boxes enclosing the plurality of 3D shapes, wherein for each surface for each tooth of the digital model, bounding boxes of a lowest tier of the hierarchy each enclose a single 3D shape, subsequent tiers of the hierarchy each enclose adjacent bounding boxes or groups of bounding boxes, and a highest tier of the hierarchy encloses all of the bounding boxes around the 3D shapes forming the surface; passing a set of tooth positions for the plurality of the patient's teeth from a treatment plan solver to a collision detector; measuring, in the collisions detector, a magnitude and a velocity of a collision between adjacent teeth when the plurality of the patient's teeth are in the set of tooth positions by identifying colliding bounding boxes at the lowest tier of the hierarchy for each of the adjacent teeth, determining the spacing or overlap between the 3D shapes bound by the bounding boxes to get the magnitude of the collision, and sequentially adjusting a position of a first tooth of the adjacent teeth by a small predetermined amount in each of a plurality of axes of the first tooth and determining the change in spacing or overlap between the 3D shapes bound by the bounding boxes to get the velocity of the collision for each of the corresponding axes; and outputting the magnitude and velocity of the collision between adjacent teeth corresponding to the set of tooth positions to the solver; outputting, from the solver, one or more orthodontic treatment plans comprising a series of dental appliances for moving the patient's teeth.
A system for automatically creating an orthodontic treatment plan of a patient may include: one or more processors; a treatment plan solver operating on the one or more processors; a collision detector operating on the one or more processors; and a memory coupled to the one or more processors, the memory configured to store computer-program instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising: forming a digital model of a surface for each tooth of a plurality of a patient's teeth by packing a plurality of 3D shapes to approximate the surface for each tooth, wherein the 3D shapes each have a core that is a line segment or a closed plane figure and an outer surface that is a constant radius from the core; forming, for the surface for each tooth of the plurality of the patient's teeth, a hierarchy of bounding boxes enclosing the plurality of 3D shapes; passing a set of tooth positions for the plurality of the patient's teeth from a treatment plan solver to a collision detector; measuring, in the collisions detector, a magnitude and a velocity of a collision between adjacent teeth when the plurality of the patient's teeth are in the set of tooth positions by identifying colliding bounding boxes and determining the spacing or overlap between the 3D shapes bound by the colliding bounding boxes; passing the magnitude and velocity of the collision between adjacent teeth corresponding to the set of tooth positions to the treatment plan solver; and outputting, from the treatment plan solver, one or more orthodontic treatment plans comprising a series of dental appliances for moving the patient's teeth.
In any of these methods and systems, the collision detector may be repeatedly (including iteratively) invoked. For example, in any of these methods and apparatuses, prior to outputting the one or more treatment plans, the steps of passing the set of tooth positions for the plurality of the patient's teeth to the collision detector, measuring the magnitude and velocity of collisions between adjacent teeth and passing the magnitude and velocity of collisions are repeated for a plurality of different sets of tooth positions, wherein the treatment plan solver generates the different sets of tooth positions.
In any of the methods and systems of generating treatment plans described herein, the treatment plan solver may generate the different sets of tooth positions while minimizing a numerical function subject to a plurality of numerical limits to get a solution vector including all stages of the one or more orthodontic treatment plans.
Also described herein are methods and apparatuses for rapid review and selection of a treatment plan, in real time, from among a large number of alternative treatment plans, including plans having different features, durations and final endpoints for the patient's dentition. Once selected/approved, the chosen treatment plan, including all of the staging information, may be transmitted for manufacture and delivery to the dental professional and/or patient. In particular, the methods and apparatuses described herein may permit the presentation of a variety of treatment plans having different treatment times (e.g., stages) and costs.
The array of treatment plans may include alternative variations of treatment plans that are presented to the user (e.g., dental professional, or in some cases, the patient) in parallel, and in real time. As mentioned, the treatment plan variations may include a variety of different stages, wherein each stage corresponds to a different aligner to be worn (e.g., for a predetermined period of time, such as for a day, a week, two weeks, etc.). The user may display side-by-side views, for real-time comparison, of different variations, and/or may swap between different variations, on a video device (computer, tablet, smartphone, etc.). The display may show the final position of the teeth predicted to result from the treatment and/or it may allow the user to review all of the different stages (including animations). The user may use one or more controls (e.g., buttons, sliders, tabs, etc.), which may be on the display to toggle between different treatment plans, including applying “filters” to show variations with or without a particular dental modification (e.g., interproximal reduction, tooth extraction, aligner attachments, etc.). Alternatively, the controls may be off of the display (e.g., on a keyboard, mouse, trackball, etc.).
As used herein, an array of treatment plans may refer to a group of treatment plans. The array of treatment plans may be unordered or ordered, and/or may be part of a single data structure or individual treatment plans may be maintained in separate data structures.
These methods and apparatus may also allow the user to modify any of the treatment plans. Many of the modifications made by the user may include variations of the treatment plans that are already pre-calculated and included in the array of treatment plans, thus the modifications may be made in real time by switching between the different treatment plan variations. The modifications may change one or more properties of the treatment and therefore the treatment plan. If the modifications go beyond the variations included in the array of treatment plans, the user may be notified, and the modified treatment plan may be transmitted back to the remote site for recalculation of the plurality of treatment plans (or the addition of new treatment plans to the array) to incorporate these changes, and the interactive method of forming and/or manufacturing the treatment plan may be continued with the new or enlarged array of treatment plan variations including the modifications requested by the user.
At the start of any of the methods described herein the dental professional may provide input, including patient-specific preferences or preferences specific to the dental professional (which may be applied to all of that dental professionals patients). Such preferences may include tooth movement restrictions (e.g., indicating which teeth should not move as part of the treatment), if interproximal reduction (IPR) should be used, and/or how, when during treatment or where to perform IPR, if attachments should be used, where (e.g., on which teeth) attachments should be placed if used, etc. In some variations, the method or apparatus may need just the name of the dental professional in order to invoke a predefined set of dental-professional specific preferences (e.g., looking up the dental professional's predefined preferences). The dental professional and/or patient may also specify which dental/orthodontic product(s) to use (e.g., which type of orthodontic product to use), which may correspond to properties that effect treatment, including the number of stages to use, the rate of movement of the teeth, etc. As with the dental-professional specific preferences, the method or apparatus may include a memory storing a database (e.g., a look-up table) of properties specific to each dental product.
In any of these methods and apparatuses, another input is typically a digital model of the patient's teeth. The digital model of the patient's teeth, as well as any of the user's patient-specific or user-specific preferences and/or the dental product(s) to be used may be used as inputs (e.g., sent to a remote site) to generate the array of treatment plans. The process of generating the treatment plans may be automated and may be fast (e.g., within a few seconds, minutes, or hours). Each treatment plan generated may include a final position, staging (e.g., a description of tooth movement directions along with a speed associated with each stage) and (optionally) a set of aligner features placed on each tooth to improve predictability of the treatment and ensure teeth movements occur. In generating each of these treatment plans, the final position of the teeth may be determined so as to address all or some of the patient's clinical conditions (e.g., malocclusions) such a crowding, bite issues, etc., and/or may approximate, as closely as possible, an ideal tooth position that may be achieved for the patient's teeth. Each treatment plan in the array may be specific to a set of properties correlated with the treatment plan and used to pre-calculate it. For example, each treatment plan may be generated using the particular set of treatment properties (“properties”). Properties may refer to modifications of the patient's teeth. For example, treatment plans may be pre-calculated for a particular number of stages/time of treatment, for the use or non-use of attachments on the teeth, for the use of attachment at particular locations, for the use of attachments of a particular type, for the use or non-use of IPR, for the use of IPR on specific sub-sets of teeth, for the use of IPR at specific stages, for the use or non-use of tooth extraction, etc., including all possible combinations of these properties. Each of the treatment plans are specific to the patient and may be independently generated for each set of properties using any of the techniques described herein. Thus, each treatment plan may be unique, and may have different tooth positions at any of the different stages and, importantly, may have different final stages. Although the treatment plans may have some generally similar, or nearly identical, tooth positions for some stages, they are typically generated independently of each other. For this reasons, these treatment plans may be referred to as partial treatment plans, since the final position of the treatment plan, and particularly those in which the maximum number of stages (and therefore the duration of the treatment), may not be the same as a comprehensive treatment plan, which resolves virtually all of a patient's orthodontic issues; instead, a partial treatment plan may partially resolve the orthodontic issues, or may resolve or partially resolve only some of these orthodontic issues
The collection (e.g., array) of treatment plans may include a matrix of different treatment plan variations. For example, the various treatment plans may include variations having different treatment times (e.g., numbers of stages, corresponding to numbers of aligners in the treatment), and for each different treatment time, the final position may be optimized to address either or both any treatment goals, as well as approximating as closely as possible an ideal or target final position. The array of treatment plans may also include treatment plans that are variations of each of the plans having different treatment numbers, in which one or more particular treatment properties (including modifications to the patient's teeth) are included (e.g., with or without IPR, with or without extractions, etc.).
Methods and apparatuses described herein may include a display providing a simple interface for treatment planning. The user may modify any of the plans with a set of tools provided as controls (buttons, etc.) that may be present on the screen or any other input. The user may switch between different pre-calculated full treatment plans already in the array of treatment plans (e.g., by applying filters on/off to show variations such as with/without IPR, with/without extractions, with/without attachments, which attachments to place on which teeth, which teeth to use IPR, changing spacing distance between teeth, changing leveling strategy from “align by incisal edge” to “align by gingiva margins,” etc.). As mentioned, in some cases, making modifications that are not covered by the pre-calculated treatment plan variations in the array of treatment plans may cause the method or apparatus to trigger generating of new treatment plans that replace or are added to the array of existing treatment plans and include the new modifications. In any of these methods and apparatuses, the treatment plans may be generated in a manner that ensures manufacturability of the plan as defined by ability to manufacture aligners based on the treatment plan without human intervention. In additional all of the treatment plans described herein may be generated so as not to worsen any orthodontic problem (e.g., malocclusion).
For example, described herein are apparatuses (e.g., systems) and methods for treatment planning that provide interactive treatment planning with a user (and/or a subject). In general, the apparatus and method may provide multiple, pre-calculated full treatment plans in which at least some of plans have different number of stages (e.g., different time to completion, wherein each stage is an aligner that may be worn for a predetermined, and continuous, amount of time), and many (if not all) of the treatment plans may have different final tooth positions that address some or all of the treatment goals. The plans may be annotated to include a description of the treatment plan, which may indicate the number of stages, the options present/absent, and the treatment goals met, treatment goals improved, or treatment goals left as-is. As described herein, these methods and apparatuses may take into account dental professional's preferences in order to maximize the treatment plans presented. For example the treatment plans that are initially presented from the array of treatment plans may be selected to have a higher probability of being acceptable to the dental professional (user), without requiring additional modifications. For example, when generating the treatment plans, the user's preferences for treatment of the specific patient (which may be based on a questionnaire and/or annotations provided by the user, as will be discussed in greater detail below, and/or treatment goals, weights on treatment goals, etc.), and/or general user preferences that may be applied to all of the user's patients (e.g., standard user practices, etc.) may be used along with the model of the patient's teeth (and in some variations the product to be used and/or characteristics of the product to be used) to generate the collection of treatment plans forming the array. Thus each array may be custom made for each individual user (e.g., dental professional) and specific to the patient. In addition, the method and apparatus may select which treatment plans to present initially based on predetermined or set user preferences.
Described herein are methods and apparatuses for the display and selection of multiple treatment plans having different endpoints and numbers of treatment stages. For example, a method of manufacturing a series of aligners for a patient's teeth may include: collecting (e.g., receiving, forming, gathering, downloading, and/or accessing, from a remote site or a local site, and may be collected, for example, in a processor to be accessed by the user), an array of treatment plans specific to the patient's teeth, wherein each treatment plan in the array describes a set of sequential stages for orthodontic movement of the patient's teeth including a final stage, further wherein at least three of the treatment plans have different numbers of sequential stages, and further wherein the array of treatment plans comprises two or more treatment plans having different treatment properties; displaying on a screen, images of the teeth at the final stage for each treatment plan of a subset of the treatment plans from the array of treatment plans; switching, in real time, between images of the teeth at the final stages for different treatment plans within the array of treatment plans based on one or more user-selected controls on the screen; and transmitting a selected one of the treatment plans for fabrication after the user has chosen the selected one of the treatment plans displayed on the screen.
The methods and apparatuses described herein may refer to transmitting and receiving to and/or from a remote site, from which the array of treatment plans specific to the patient's teeth may be generated, the remote site may be remote from the user, and may be accessible via a web (e.g., cloud) server, or the like. In some variations the treatment plans are generated locally, e.g., on software that is running on the user's computer/processor, which is the same processor containing instructions (e.g., code) for executing the interactive treatment planning, including receiving the array of treatment plans.
As mentioned, each treatment plan in the array may describe a set of sequential stages for orthodontic movement of the patient's teeth including a final stage. The treatment plan may include the final position of the patient's teeth, staging for the movements of the patient's teeth (e.g., a description of tooth movement directions along with a speed associated with each stage, which may be key frames, showing relative movement of the teeth over the treatment), and a set of aligner features placed on each tooth to improve predictability of the treatment and ensure teeth movements to happen. The treatment plan may also include metadata (e.g., annotations) about the number of stages, presence/absence of tooth modifications, effect on the patient's treatment goals, etc.). a
As used herein, the phrase “real time” may refer to the immediate (or a perception of immediate or concurrent) response, for example, a response that is within milliseconds so that it is available virtually immediate when observed by the user. Near real time may refer to within a few seconds to a few minutes of concurrent.
The screen used to display and interact with the user may be any appropriate screen or monitor, including touchscreen and non-touchscreen screens. The screen may be part of a laptop, desktop, or other computer, including hand-held (e.g., smartphone, tablet, etc.) screens. The screen may include flat panel displays as well as other displays, including virtual reality (e.g., glasses, goggles, etc.) and projections (e.g., surface projections).
The different treatment properties that may be used to generate the various treatment plans may include one or more of: interproximal reduction (IRR)/changing spacing distance between teeth, extraction, and aligner attachments (one or more, at various locations), changing leveling strategy from “align by incisal edge” to “align by gingiva margins,” etc.
As will be described in more detail below, any of the methods described herein may include generating the plurality of treatment plans, such as generating the array of full treatment plans including variations of different treatment properties. These treatment plans may be pre-calculated. Any number of treatment plans may be included in the array of treatment plans, typically 3 or more (e.g., 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 22 or more, 24 or more, 26 or more, 28 or more, 30 or more, 35 or more, 40 or more, 45 or more, 50 or more, 55 or more, 60 or more, 65 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more, etc.). In practice, any of these methods may include transmitting a model of the patient's teeth to the remote site holding the treatment plan generator, which may be referred to as a treatment plan optimizing engine or treatment plan optimizing generator. The treatment plan generator may be located, e.g., as part of a processor or other system, at a remote site. Thus, any of these methods may include generating, at the remote site, the array of treatment plans specific to the patient's teeth. The model of the patient's teeth (upper arch, lower arch, or upper and lower arch) may be transmitted to the remote site and may be received there as a digital file or it may be digitized at the remote site. The remote site may be a dental laboratory. In addition, the user may transmit a list of tooth movement prescription information (e.g. patient-specific treatment preferences), wherein the list of tooth movement prescription information comprises tooth movement limitations, retraction limitations, interproximal reduction limitations. In any of these variations, the remote site may also receive the users identity and may look up stored (e.g., in a database) user-specific preferences that may be applied in any case associated with that user (e.g., user-specific treatment preferences). The user may also (optionally) transmit treatment details or a reference to a set of treatment details, including product details specific to the one or more types of dental/orthodontic products to be used, if not included, may be set by default. For example, the user may transmit as input to the treatment plan generator, the name(s) or indicator(s) of particular dental/orthodontic treatment program(s), such as “OrthoGO.” The treatment plan generator may then use this name or indicator to look up details specific to this treatment program from a memory accessible or included in the treatment plan generator such as the maximum number of aligners (e.g., the maximum duration of treatment), etc.
The user may be shown one or, more preferably, multiple treatment plans for their review (in real time or near real time). In displaying the treatment plans, the user may be shown the final stage of the treatment plan (showing the final position of the patient's teeth, as achieved by the treatment plan), and/or may be shown the metadata about the treatment plan (e.g., the number of stages, the treated/untreated components, etc.). Displaying images of the teeth at the final stage for each treatment plan of the subset of the treatment plans from the array of treatment plans may include concurrently displaying images of the teeth at the final stages for treatment plans have different numbers of sequential stages. The treatment plans may be shown side-by-side for comparison. In some variations, the user may toggle between “filters” or switches showing the different treatment plans (e.g., a plan with or without IPR, etc.). Because the treatment plans have been pre-calculated and are in the array, they may be shown in real time, and the user may easily switch between different versions, allowing the user to pick an optimal treatment for the patient.
As mentioned, switching between images of the teeth at the final stages for different treatment plans within the array of treatment plans may be based on one or more user-selected (selectable) controls on the screen, such as switching a treatment plan having a first number of sequential stages with a treatment plan having the same number of sequential stages but having different treatment properties when the user changes one or more user-selected controls on the screen.
For example, different filters/switches may toggle between treatment plans showing one or more tooth/treatment modifications. Virtually any modification may be used, including, for example: one or more of: extractions (of one or more teeth), adjusting of tooth overjet, adjusting for overbites, adjusting for cross bites, interproximal reductions (IPR), including one o or more aligner attachments on the teeth, and anterior to posterior (A-P) correction. These filters may correspond to treatment preferences.
The methods and apparatuses described herein may include one or more tools allowing the user to modify one or more of the treatment plans. Tools may be display tools (e.g., allowing rotation, sectioning, etc.) for modifying the display of the tooth shown by the treatment plan(s), or they may modify the treatment plan itself, e.g., modifying the position and/or orientation of the teeth in the final (or any intermediate) stage of the treatment plan. Tools may include adding/removing attachments on the teeth. The tools may include removing (e.g., reducing) material from the side of the tooth/teeth, and/or removing (e.g., extracting) the teeth.
Any of these methods may include transmitting the modified one or more treatment plans to the treatment plan optimizing generator (e.g., the remote site where the treatment plan optimizing generator is located) to recalculate the array of treatment plans based on the modifications of the one or more treatment plans.
Any of these methods may also allow the user to walk through the entire treatment plan, either as a key frame display and/or as a more intuitive display of a model (e.g., 3D model or projection) of the user's teeth in each stage. Thus, any of these methods may include displaying, on the screen, a plurality of the sequential stages (e.g., images of the teeth at each stage) when the user selects a stage selection control. These displays may be animations, showing movements of the teeth.
Any of the methods and apparatuses describe herein may allow the user to select a subset of treatment plans from the larger array (set) of treatment plans. This subset may be used for presentation to a patient, as described below (e.g., in a ‘consulting mode’) or for further modification or refinement, including for side-by side comparison. In some variations, the method or apparatus may automatically select the one or more treatment plans for display to the user, based on predefined user preferences. For example, any of these methods and apparatuses may include selecting the subset of the treatment plans from the array of treatment plans to display based on user preferences.
As described above, toggling the display between treatment plans may be performed by turning on/off one or more “filters” or toggles. For example, any of these methods and apparatuses may be configured to filter one or more of the displayed images of the teeth at the final stage of the subset of the treatment plans from the array of treatment plans when the user selects a filter control.
For example, a method of manufacturing a series of aligners for a patient's teeth may include: transmitting a model of the patient's teeth to a remote site; transmitting a list of tooth movement prescription information to the remote site; collecting (e.g., receiving, gathering, downloading, and/or accessing) from the remote site, an array of treatment plans specific to the patient's teeth, wherein each treatment plan in the array describes a set of sequential stages for orthodontic movement of the patient's teeth having a final stage, further wherein at least three of the treatment plans have different numbers of sequential stages, and further wherein, for each number of sequential stages, the array comprises two or more treatment plans having the same number of sequential stages but treatment properties; displaying on a screen, images of the teeth at the final stage for each treatment plan of a subset of the treatment plans from the array of treatment plans; switching, in real time, between images of the teeth at the final stages for different treatment plans within the array of treatment plans based on one or more user-selected controls on the screen; and transmitting a selected one of the treatment plans for fabrication after the user has chosen the selected one of the treatment plans displayed on the screen.
Any of the methods described herein may be performed by an apparatus configured to perform the method steps. In particular, described herein are apparatuses (e.g., systems) that are configured to execute instructions (code) to control an apparatus having a processor, display, input, etc. to interactively allow a user to design and manufacture a series of aligners for correction of malocclusions of a patient's teeth. Any of these apparatuses may be configured as non-transient, computer-readable media (e.g., software, firmware, hardware or some combination thereof) that includes instructions for controlling the apparatus as described herein and may be part of a system or sub-system.
For example, described herein are non-transient, computer-readable media containing program instructions for causing a computer to: receive, from a remote site, an array of treatment plans specific to the patient's teeth, wherein each treatment plan in the array describes a set of sequential stages for orthodontic movement of the patient's teeth including a final stage, further wherein at least three of the treatment plans have different numbers of sequential stages, and further wherein the array of treatment plans comprises two or more treatment plans having different treatment properties; display on a screen, images of the teeth at the final stage for each treatment plan of a subset of the treatment plans from the array of treatment plans; switch, in real time, between images of the teeth at the final stages for different treatment plans within the array of treatment plans based on one or more user-selected controls on the screen; and transmit a selected one of the treatment plans for fabrication after the user has chosen the selected one of the treatment plans displayed on the screen. Also described herein are systems including one or more processors configured to execute the program instructions. These systems may be referred to herein as treatment plan comparators (or interactive, real-time treatment plan comparators), and may optionally or additionally include one or more displays, one or more user selectable controls and one or more communications circuitry (e.g., a communications module, such as a wireless circuit, etc.).
The different treatment properties that may be varied between the different treatment plans may include treatment preferences, such as one or more of: allowing or restriction interproximal reduction (IRR), allowing or restriction extraction, and allowing or restricting aligner attachments (e.g., attachments on the teeth to couple to the aligner). The program instructions may be further configured to transmit a model of the patient's teeth to the remote site. The program instructions may be further configured to, generate at the remote site, the array of treatment plans specific to the patient's teeth.
The program instructions may be configured to transmit a list of tooth movement prescription information (e.g., to the remote site containing the treatment plan optimizing generator), wherein the list of tooth movement prescription information may include tooth movement limitations, retraction limitations, interproximal reduction limitations.
The program instructions may be configured to display images of the teeth at the final stage for each treatment plan of the subset of the treatment plans from the array of treatment plans by concurrently displaying images of the teeth at the final stages for treatment plans have different numbers of sequential stages.
The program instructions may be configured to switch between images of the teeth at the final stages for different treatment plans within the array of treatment plans based on one or more user-selected controls on the screen by switching a treatment plan having a first number of sequential stages with a treatment plan having the same number of sequential stages but having different treatment properties based on one or more user-selected controls on the screen. The different treatment properties may include one or more of: extractions, overjets, overbites, cross bites, interproximal reductions, attachments, and anterior to posterior (A-P) correction. The program instructions may be further configured to provide one or more tools allowing the user to modify one or more of the treatment plans.
The program instructions may be further configured to transmit the modified one or more treatment plans to the remote site to recalculate the array of treatment plans based on the modifications of the one or more treatment plans. The program instructions may be further configured to display, on the screen, a plurality of the sequential stages when the user selects a stage selection control. The stage selection control may allow the user to dynamically, in real time, view the different stages (e.g., positions of the teeth in each stage).
The program instructions may be further configured to select the subset of the treatment plans from the array of treatment plans to display based on user preferences. The program instructions may be further configured to filter one or more of the displayed images of the teeth at the final stage of the subset of the treatment plans from the array of treatment plans when the user selects a filter control.
As mentioned, any of these methods and apparatuses may include a consultation mode that presents multiple treatment plans to the patient or physician, including treatment plans having different numbers of stages and different final tooth positions. These displayed treatment plans may be selected by the user (e.g., dental professional) as a subset of the treatment plans in the array. The displayed treatment plans (and any corresponding metadata about them) may be displayed sequentially or simultaneously (e.g., side-by-side) for viewing by the patient. Pricing information may also be shown corresponding to each treatment plan, along with a graphical display that allows the patient (and/or user) to see the final tooth positions and allows the user to select the desired outcome. Multiple treatment plans having a different number of stages and different final tooth positions may be presented to the patient, and the patient (by themselves or in consultation with the dental professional) may decide which treatment plan to select.
Once a treatment plan is selected, it may be used to generate a series of aligners. For example, the treatment plan may be transmitted to a manufacturing facility that may directly print (e.g., by 3D printing) each aligner in the series, or the treatment plan may be used to generate a positive model of the patient's teeth at each stage used for forming the aligners (e.g., by thermoforming).
Thus, in consultation mode the patient is presented with multiple treatment plans having different numbers of stages and allowed to select which treatment plan to manufacture and use.
For example, a method of manufacturing a series of aligners for a patient's teeth may include a consultation mode. For example, the method may include: collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), in a processor, an array of treatment plans specific to the patient's teeth, wherein each treatment plan in the array describes a set of sequential stages for orthodontic movement of the patient's teeth, including a final stage, further wherein at least three of the treatment plans have different numbers of sequential stages, and further wherein the final stages of the treatment plans within the array of treatment plans are different; displaying, on a screen, images of the teeth at the final stages for a subset of the treatment plans from the array of treatment plans; selecting, by a user, two or more treatment plans from the subset of the treatment plans using one or more controls on the screen; and displaying, to the patient, the two or more treatment plans from the subset of the treatment plans; and transmitting a selected one of the two or more treatment plans for fabrication after the patient has chosen the selected one of the treatment plans.
As mentioned, any of these method may include transmitting a model of the patient's teeth to a remote site (e.g., the location of the treatment plan optimizing generator). Displaying the two or more treatment plans may include displaying annotations describing changes in the final stage of each of the two or more treatment plans compared to the model of the patient's teeth. The annotations (which may be “metadata” about the treatment plan) may comprise one or more of: changes in the malocclusion, changes in the patient's bite, and changes in the upper and/or lower crowding.
Displaying, to the patient, the two or more treatment plans from the subset of the treatment plans may comprise displaying the number of sequential stages.
Any of these methods may include switching, in real time, one or more of the images of the teeth at the final stages of the subset for different treatment plans with an image of the final stage of one or more other treatment plans from the array of treatment plans, based on one or more user-selected controls on the screen. The two or more treatment plans from the subset of the treatment plans may comprise two or more treatment plans having different numbers of sequential stages.
For example, a method of manufacturing a series of aligners for a patient's teeth may include: transmitting a model of the patient's teeth to a remote site; collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), from the remote site, an array of treatment plans specific to the patient's teeth, wherein each treatment plan in the array describes a set of sequential stages for orthodontic movement of the patient's teeth, including a final stage, further wherein at least three of the treatment plans have different numbers of sequential stages, and further wherein the final stages of the treatment plans within the array of treatment plans are different; displaying, on a screen, images of the teeth at the final stages for a first subset of the treatment plans from the array of treatment plans; selecting, by a user, two or more treatment plans from the first subset of the treatment plans using one or more controls on the screen; displaying, to the patient, the two or more treatment plans and annotations describing changes in the final stage of each of the two or more treatment plans compared to the model of the patient's teeth; and transmitting a selected one of the two or more treatment plans for fabrication after the patient has chosen the selected one of the treatment plans.
Also described herein are non-transient, computer-readable medium containing program instructions for causing a computer to: receive, in a processor, an array of treatment plans specific to the patient's teeth, wherein each treatment plan in the array describes a set of sequential stages for orthodontic movement of the patient's teeth, including a final stage, further wherein at least three of the treatment plans have different numbers of sequential stages, and further wherein the final stages of the treatment plans within the array of treatment plans are different; display, on a screen, images of the teeth at the final stages for a first subset of the treatment plans from the array of treatment plans; select, by a user, two or more treatment plans from the first subject of the treatment plans using one or more controls on the screen; and display, to the patient, the two or more treatment plans; and transmit a selected one of the two or more treatment plans for fabrication after the patient has chosen the selected one of the treatment plans.
In general, the methods and apparatuses described herein provide interactive treatment planning, and may present multiple, full and pre-calculated treatment plans to a user (e.g., dental professional) and allows the user to switch between views of different pre-calculated treatment plans, and to modify the one or more treatment plans. The systems and methods described herein may also allow for the doctor to change final position and re-calculate a new final position real time without sending it to the technician
Also described herein are methods and apparatuses (e.g., non-transient, computer-readable medium containing program instructions for causing a computer to perform steps) for creating one or more (e.g., an array of) treatment plans to align, including partially aligning, a patient's teeth.
For example, described herein are automated methods of creating a treatment plan to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages, the method comprising: collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), in a processor: a digital model of a patient's teeth, a set of treatment preferences and/or a reference to a set of treatment preferences, a comprehensive final position of the patient's teeth, and (optionally) a set of treatment details or an identifier identifying the set of treatment details; selecting a plurality of numerically expressed treatment targets and constraints from a memory accessible to the processor, based on the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth; combining the plurality of numerically expressed treatment targets to form a single numerical merit function; selecting a plurality of numeric limits on the treatment constraint functions based on the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth; minimizing the single numerical merit function to get a solution vector including all stages forming the treatment plan, subject to the plurality of limits on numeric treatment constraints; and mapping the solution vector to a treatment plan, wherein the treatment plan includes a final tooth position that is different from the comprehensive final position of the patient's teeth.
In general, treatment details may refer to a product definition, which may be the parameters set by the properties of the aligner product to be used for the treatment, e.g., the number of stages, the rate of tooth movement, etc. Treatment preferences may refer to the treatment preferences of the dental practitioner (e.g., which teeth not to move, etc.) and may be specific to the patient, or may be specific to the user and applied to all of the user's patient's. Thus, treatment details may include details about the product(s) that may be used to achieve the treatment, including the number and type of aligners, and any properties of the aligners themselves. In some variations, the treatment details are not provided separately to the method or apparatus, but are available (e.g., as a default) when generating the one or more treatment plans. The method or apparatus may default to a single set of treatment details, corresponding to a single dental/orthodontic product, or it may be selected from a listing of predefined products having specified properties. Alternatively, the user may provide the details specific to a particular product or products, forming the treatment details. Note that when a plurality of different treatment plans are to be generated, forming an array of treatment plans, the various sets of treatment details, corresponding to different dental/orthodontic products, may be used to generate different variations.
Examples of treatment preferences are described in detail herein. For example, treatment preferences may include one or more of: an indicator of which teeth are not permitted to move, an indication of which teeth should not have an attachment, an indicator of which teeth to treat, an indicator of tooth class correction amount, an indicator that interproximal reduction is to be used, an indicator that arch expansion is to be used, and indicator of spacing between teeth, an indicator or tooth levelling. The identifier identifying the treatment details may identify a product having a defined set of treatment details accessible to the processor.
Examples of treatment details (corresponding to the properties of different products that may be used) are also provided below, but may include one or more of: a maximum allowed number of stages, whether attachments to the patient's teeth are allowed, a maximum allowed tooth root movement, a maximum allowed tooth crown movement, and a maximum allowed tooth rotation.
Combining the numerically expressed treatment targets may mean weighting each of the numerically expressed treatment targets in the single numerical function. A weight factor may be used to multiple any of the numerically expressed treatment targets; different weight factors may be used for each or a subset of the numerically expressed treatment targets. Weighting factors may be set empirically or may be adjusted by the apparatus.
The single numerical function may include, for a set of teeth, a sum of at least: a difference from the positions of the teeth compared to the comprehensive final position of the patient's teeth, a measure of misalignment in an x direction for the teeth, a measure of misalignment in a z direction for the teeth, a measure of misalignment of a dental arch of the teeth, a measure of diastema between neighboring teeth, a measure of overjet of the teeth, a measure of overbite of the teeth, a measure of collisions between the teeth, a measure of the difference between an arch of the teeth and the comprehensive final position of the patient's teeth, a measure of the difference in leveling between the teeth and the comprehensive final position of the patient's teeth, a measure of the amount of occlusion between the teeth of the patient's upper and lower jaws, a measure of the difference in the amount of occlusion between the teeth and the comprehensive final position of the patient's teeth, a measure of the amount of mesial to distal round trips of the teeth, a measure of the amount of buccal to lingual round trips of the teeth, and a measure of a number of aligner stages compared to a target number of aligner stages from the set of treatment details. For example, the single numerical function may include, for a set of teeth, a sum of at least: a difference from the positions of the teeth compared to the comprehensive final position of the patient's teeth, a measure of misalignment for the teeth, and a measure of a number of aligner stages compared to a target number of aligner stages from the set of treatment details.
The methods described herein may also include adjusting the plurality of numerically expressed treatment targets into a plurality of adjusted numerically expressed treatment targets based on: the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth, further wherein combining the plurality of numerically expressed treatment targets comprises combining the plurality of adjusted numerically expressed treatment targets.
The plurality of numeric limits may comprise one or more of: a maximum velocity of tooth movement, a maximum amount of collision between teeth, a tooth movement limitation, a maximum number of aligner stages, a maximum amount of occlusion, a maximum amount of occlusion, a maximum amount of overbite, a maximum amount of overjet, and a maximum midline position.
In general, minimizing the single numerical function subject to the plurality of numeric limits may comprise using a constrained optimization method to get a solution vector. The constrained optimization method may comprise an interior point method (e.g., interior point method variations such as SQP and Active Set).
Mapping the solution vector to a treatment plan may comprises converting the solution vector into a set of key frames for each tooth, corresponding to a stage number, and positional information for each tooth, including an x coordinate, a y coordinate, a z coordinate, and an angulation, an inclination and a rotation angle. In mapping the solution vector to form a key function, the solution vector may include numerous key function components that may be combined to form the complete key function set for the treatment plan. Thus, mapping the solution vector to a treatment plan may include, for some teeth, mapping a single variable in the solution vector to a single coordinate or angle in a key frame, while for some teeth, mapping may include mapping a linear combination of multiple variables from the solution vector to a single coordinate or angle in a key frame.
The method may also include displaying the final tooth position of the treatment plan and/or transmitting the treatment plan to be displayed.
For example, an automated method of creating a treatment plan to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages, may include: collecting (e.g., receiving, forming, gathering, downloading, and/or accessing), in a processor: a digital model of a patient's teeth; accessing (by the processor) a set of treatment preferences, a comprehensive final position of the patient's teeth, and a set of treatment details; selecting a plurality of numerically expressed treatment targets from a memory accessible to the processor based on: the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth; adjusting the plurality of numerically expressed treatment targets into a plurality of adjusted numerically expressed treatment targets based on: the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth; combining the plurality of adjusted numerically expressed treatment targets to form a single numerical function; setting a plurality of numeric limits on the single numerical function based on the set treatment preferences; minimizing (e.g., iteratively) the single numerical function subject to the plurality of numeric limits to get a solution vector including all stages forming the treatment plan; and mapping the solution vector to a treatment plan, wherein the treatment plan includes a final tooth position that is different from the comprehensive final position of the patient's teeth.
Accessing the set of treatment preferences may include collecting (e.g., receiving, forming, gathering, downloading, and/or accessing in the processor) the set of treatment preferences, or collecting a reference to a set of treatment preferences that the processor may use to look up a set of (e.g. one or more) treatment preferences from a memory accessible by the processor holding, for example, a look-up table of preferences indexed by a reference. Accessing the comprehensive final position of the patient's teeth may include receiving the comprehensive final position (e.g., as a digital model or representation of positions of the patient's teeth), or it may include generating, using the processor, the comprehensive final position. The comprehensive final position may be manually or semi-manually generated and a digital copy sent to the processor. If the processor has already generated the comprehensive final position, the processor may store it in a memory and access it later (e.g., during additional cycles). Accessing the set of treatment details may include accessing a stored (in a memory) set of treatment details, including accessing a ‘default’ set of treatment details, receiving the set of treatment details, or it may include receiving an identifier identifying the set of treatment details and using the identifier to look up, from a memory (e.g. holding a look-up table) a set of treatment details. The identifier may be a product name/model, etc.
Also described herein are apparatuses for performing any of these methods as a non-transient, computer-readable medium containing program instructions for creating a treatment plan to align a patient's teeth using a plurality of removable aligners. For example, the program instructions may cause a processor to: receive, in the processor: a digital model of a patient's teeth, a set of treatment preferences or a reference to a set of treatment preferences, a comprehensive final position of the patient's teeth, and a set of treatment details or an identifier identifying the set of treatment details; select a plurality of numerically expressed treatment targets from a memory accessible to the processor, based on the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth; combine the plurality of numerically expressed treatment targets to form a single numerical function; select a plurality of numeric limits on the single numerical function based on the set treatment preferences; minimize the single numerical function subject to the plurality of numeric limits to get a solution vector including all stages forming the treatment plan; and map the solution vector to a treatment plan, wherein the treatment plan includes a final tooth position that is different from the comprehensive final position of the patient's teeth.
As mentioned above, the treatment preferences may comprise one or more of: an indicator of which teeth are not permitted to move, an indication of which teeth should not have an attachment, an indicator of which teeth to treat, an indicator of tooth class correction amount, an indicator that interproximal reduction is to be used, an indicator that arch expansion is to be used, and indicator of spacing between teeth, an indicator or tooth levelling. The identifier identifying the treatment details may identify a product having a defined set of treatment details accessible to the processor.
The set of treatment details may comprises one or more of: a maximum allowed number of stages, whether attachments to the patient's teeth are allowed, a maximum allowed tooth root movement, a maximum allowed tooth crown movement, and a maximum allowed tooth rotation. Combining the numerically expressed treatment targets may further comprise weighting each of the numerically expressed treatment targets in the single numerical function.
The single numerical function may include, for a set of teeth, a sum of at least: a difference from the positions of the teeth compared to the comprehensive final position of the patient's teeth, a measure of misalignment in an x direction for the teeth, a measure of misalignment in a z direction for the teeth, a measure of misalignment of a dental arch of the teeth, a measure of diastema between neighboring teeth, a measure of overjet of the teeth, a measure of overbite of the teeth, a measure of collisions between the teeth, a measure of the difference between an arch of the teeth and the comprehensive final position of the patient's teeth, a measure of the difference in leveling between the teeth and the comprehensive final position of the patient's teeth, a measure of the amount of occlusion between the teeth of the patient's upper and lower jaws, a measure of the difference in the amount of occlusion between the teeth and the comprehensive final position of the patient's teeth, a measure of the amount of mesial to distal round trips of the teeth, a measure of the amount of buccal to lingual round trips of the teeth, and a measure of a number of aligner stages compared to a target number of aligner stages from the set of treatment details.
The non-transient, computer-readable medium of claim 15, wherein the single numerical function includes, for a set of teeth, a sum of at least: a difference from the positions of the teeth compared to the comprehensive final position of the patient's teeth, a measure of misalignment for the teeth, and a measure of a number of aligner stages compared to a target number of aligner stages from the set of treatment details.
The program instructions may be further configured to adjust the plurality of numerically expressed treatment targets into a plurality of adjusted numerically expressed treatment targets based on: the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth. Further, the program instructions may also be configured to combine the plurality of numerically expressed treatment targets by combining the plurality of adjusted numerically expressed treatment targets.
The plurality of numeric limits may comprise one or more of: a maximum velocity of tooth movement, a maximum amount of collision between teeth, a tooth movement limitation, a maximum number of aligner stages, a maximum amount of occlusion, a maximum amount of occlusion, a maximum amount of overbite, a maximum amount of overjet, and a maximum midline position. The program instructions may be further configured to minimize the single numerical function subject to the plurality of numeric limits using a constrained optimization method to get a solution vector.
The program instructions may be further configured to map the solution vector to a treatment plan by converting the solution vector into a set of key frames for each tooth, corresponding to a stage number, and positional information for each tooth, including an x coordinate, a y coordinate, a z coordinate, and an angulation, an inclination and a rotation angle.
The program instructions may be configured to provide the final tooth position of the treatment plan for display.
Any of the methods and apparatuses described herein may be configured to generate an array of treatment plan variations. This may be achieved, for example, by modifying, either automatically or manually, the treatment preferences and/or treatment details. For example, the maximum number of stages (corresponding to the number of aligners to be used in a treatment) may be modified to generate treatment plan variations having different treatment durations, since the duration of treatment is typically correlated to the number of aligners to be worn. Although in general, the more stages/aligners used, the greater the overall amount of correction that may be achieved, as described herein (e.g., approaching a comprehensive final position of the teeth which may be considered an optimal treatment plan) in some cases it may be preferable by the patient and/or dental professional to limit the duration of treatment and settle for an improved, but not perfectly corrected, alignment.
Also described herein are methods of modifying a treatment plan for a series of aligners and/or manufacturing a series of aligners for a patient's teeth. For example a method may include: transmitting a model of the patient's teeth to a remote site; transmitting a list of tooth movement prescription information to the remote site; collecting a plurality of treatment plans specific to the patient's teeth using an ideal final position for the patient's teeth and the model of the patient's teeth, wherein each treatment plan in the plurality of treatment plans describes a set of sequential stages for orthodontic movement of the patient's teeth having a final stage, further wherein, for each treatment plan the final stage is different from the ideal final position for each of the different treatment properties of the patient's teeth; ranking the plurality of treatment plans based on how comprehensive they are compared to the ideal final position; and displaying on a screen, images of the patient's teeth at the final stage for either the first or the first and second treatment plans from the array of treatment plans; switching, in real time, between images of the teeth at the final stages for different treatment plans within the array of treatment plans based on one or more user-selected controls on the screen; and transmitting a selected one of the treatment plans for fabrication after the user has chosen the selected one of the treatment plans displayed on the screen.
The different treatment properties may comprise one or more of: interproximal reduction (IRR), extraction, and aligner attachments. For example, the tooth movement prescription information may comprise tooth movement limitations, retraction limitations, interproximal reduction limitations.
Any of these methods may include switching between images of the teeth at the final stages for different treatment plans within the array of treatment plans based on one or more user-selected controls on the screen by switching a treatment plan having a first number of sequential stages with a treatment plan having the same number of sequential stages but having different treatment properties based on one or more user-selected controls on the screen.
Ranking based on how comprehensive the treatment plan is may include looking up a score from a database of rankings indexed by two or more of: interproximal reductions, attachments, dental aligner product, extractions, overjets, overbites, cross bites, and anterior to posterior (A-P) correction. Alternatively or additionally, ranking based on how comprehensive the treatment plan is may include scoring the treatment plan based on three or more of: interproximal reductions, attachments, dental aligner product, extractions, overjets, overbites, cross bites, and anterior to posterior (A-P) correction.
The method may also include modifying one or more of the treatment plans using the user interface and transmitting the modified one or more treatment plans to the remote site to recalculate the array of treatment plans based on the modifications of the one or more treatment plans.
The novel features of the invention are set forth with particularity in the claims that follow. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:
In general, described herein are methods and apparatuses for manufacturing a series of aligners for a patient's teeth that may include generating multiple treatment plans that are limited various specified stages (e.g., 5 stages, 6 stages, 7 stages, 8 stages, 9 stages, 10 stages, 10 stages, 12 stages, 14 stages, 15 stages, 16 stages, 17 stages, 18 stages, 19 stages, 20 stages, 21 stages, 22 stages, 23 stages, 24 stages, 25 stages, 26 stages, 27 stages, 28 stages, 29 stages, 30 stages, etc.) and variations of these fixed-stage treatment plans in which one or more features are included to a predetermined degree (e.g., interproximal reduction, use of some number of aligner attachments, etc.). These methods and apparatuses may also include interactively displaying the multiple treatment plans, and allowing a user, such as a dental professional (e.g., doctor, dentist, orthodontist, etc.) to view, select and/or modify the multiple treatment plans. The multiple treatment plans may be labeled to indicate what treatment goals they do or do not address. The user may also select a subset of the multiple treatment plans for inclusion as part of a patient consultation, displaying the treatment plans for comparison and selection by patient.
A treatment plan optimizing generator, described in greater detail below, may be used to generate a plurality of treatment plans that are variations of each other. Typically the input to the treatment plan optimizing generator is a digital scan of the patient's teeth, as well as the constraints (e.g., number of stages, tooth modifications features, etc.) and preferences, and an “ideal” alignment of the patient's teeth (which may be manually, automatically or semi-automatically generated). The treatment plan optimizing generator may then automatically generate a treatment plan that is limited by those constraints, and that both addresses one or more treatment goals (which may also be identified or automatically identified) and is as close to the ideal alignment as possible. The treatment plan optimizing generator may be used multiple times to automatically generate a plurality of treatment plan variations that may be collected into an array (or group) of treatment plans.
As will be described in greater detail here, the results of the multiple treatment plan generation may be presented to a user. The multiple treatment plans may be collected as an array of multiple treatment plans that may include metadata identifying each treatment plan and/or the treatment goal that it addresses or does not address. Each treatment plan may represent a clinically feasible treatment plan. Further, for each plan there may be several options available to modify the plan. Plans may be limited to the number of stages, which may correlate to a commercial product. The product may include restrictions (product limitations) which may be included in the treatment plan. For example treatment plans may correspond to low stage plans (e.g. between 5 and 13 stages), intermediate stage plans (between 14 and 25 stages) and high intermediate stage plans (26 and more stages). Other tooth modification features may also be included as limitations modifying the treatment plans, such as including or not including aligner attachment placement. If attachments are not allowed, then a restricted clinical protocol may be applied to avoid unpredictable movements. Another example of a tooth modification feature that may be included in the treatment plan is to include IPR or not include IPR. If IPR is not allowed then the best plan may be presented with a condition that IPR is not allowed during the duration of the treatment.
In addition, template selection may select a clinical protocol to be applied for plan generation. The user may select any combination of options in order to determine which treatment plan is the best, given the constraints provided. All possible combinations of the plans are pre-calculated so the user can see, in real time, the options available by changing to a different clinical filter without the need to redo the treatment plan. The user may also modify any of the treatment plans with 3D controls, in which each change made with tools modifies a plan but is configured to keeps the ability to transfer the selected (and modified) treatment plan directly to manufacturing (e.g., without further human intervention).
In general, the methods described here are directed to the manufacture of a series or sequence of orthodontic aligner appliances that maybe worn sequentially to correct malocclusion(s). For example,
Optionally, in cases involving more complex movements or treatment plans, it may be beneficial to utilize auxiliary components (e.g., features, accessories, structures, devices, components, and the like) in conjunction with an orthodontic appliance. Examples of such accessories include but are not limited to elastics, wires, springs, bars, arch expanders, palatal expanders, twin blocks, occlusal blocks, bite ramps, mandibular advancement splints, bite plates, pontics, hooks, brackets, headgear tubes, springs, bumper tubes, palatal bars, frameworks, pin-and-tube apparatuses, buccal shields, buccinator bows, wire shields, lingual flanges and pads, lip pads or bumpers, protrusions, divots, and the like. Additional examples of accessories include but are not limited to opposing arch features, occlusal features, torsional rigidity features, occlusal cusp, and bridges. In some embodiments, the appliances, systems and methods described herein include improved orthodontic appliances with integrally formed features that are shaped to couple to such auxiliary components, or that replace such auxiliary components.
The various embodiments of the orthodontic appliances presented herein can be fabricated in a wide variety of ways. In some embodiments, the orthodontic appliances herein (or portions thereof) can be produced using direct fabrication, such as additive manufacturing techniques (also referred to herein as “3D printing) or subtractive manufacturing techniques (e.g., milling). In some embodiments, direct fabrication involves forming an object (e.g., an orthodontic appliance or a portion thereof) without using a physical template (e.g., mold, mask etc.) to define the object geometry. For example, stereolithography can be used to directly fabricate one or more of the appliances herein. In some embodiments, stereolithography involves selective polymerization of a photosensitive resin (e.g., a photopolymer) according to a desired cross-sectional shape using light (e.g., ultraviolet light). The object geometry can be built up in a layer-by-layer fashion by sequentially polymerizing a plurality of object cross-sections. As another example, the appliances herein can be directly fabricated using selective laser sintering. In some embodiments, selective laser sintering involves using a laser beam to selectively melt and fuse a layer of powdered material according to a desired cross-sectional shape in order to build up the object geometry. As yet another example, the appliances herein can be directly fabricated by fused deposition modeling. In some embodiments, fused deposition modeling involves melting and selectively depositing a thin filament of thermoplastic polymer in a layer-by-layer manner in order to form an object. In yet another example, material jetting can be used to directly fabricate the appliances herein. In some embodiments, material jetting involves jetting or extruding one or more materials onto a build surface in order to form successive layers of the object geometry.
In some embodiments, the direct fabrication methods provided herein build up the object geometry in a layer-by-layer fashion, with successive layers being formed in discrete build steps. Alternatively or in combination, direct fabrication methods that allow for continuous build-up of an object's geometry can be used, referred to herein as “continuous direct fabrication.” Various types of continuous direct fabrication methods can be used. Continuous liquid interphase printing is described in U.S. Patent Publication Nos. 2015/0097315, 2015/0097316, and 2015/0102532, the disclosures of each of which are incorporated herein by reference in their entirety.
As another example, a continuous direct fabrication method can achieve continuous build-up of an object geometry by continuous movement of the build platform (e.g., along the vertical or Z-direction) during the irradiation phase, such that the hardening depth of the irradiated photopolymer is controlled by the movement speed. Accordingly, continuous polymerization of material on the build surface can be achieved. Such methods are described in U.S. Pat. No. 7,892,474, the disclosure of which is incorporated herein by reference in its entirety.
In another example, a continuous direct fabrication method can involve extruding a composite material composed of a curable liquid material surrounding a solid strand. The composite material can be extruded along a continuous three-dimensional path in order to form the object. Such methods are described in U.S. Patent Publication No. 2014/0061974, the disclosure of which is incorporated herein by reference in its entirety.
In yet another example, a continuous direct fabrication method utilizes a “heliolithography” approach in which the liquid photopolymer is cured with focused radiation while the build platform is continuously rotated and raised. Accordingly, the object geometry can be continuously built up along a spiral build path. Such methods are described in U.S. Patent Publication No. 2014/0265034, the disclosure of which is incorporated herein by reference in its entirety.
In some embodiments, relatively rigid portions of the orthodontic appliance can be formed via direct fabrication using one or more of the following materials: a polyester, a co-polyester, a polycarbonate, a thermoplastic polyurethane, a polypropylene, a polyethylene, a polypropylene and polyethylene copolymer, an acrylic, a cyclic block copolymer, a polyetheretherketone, a polyamide, a polyethylene terephthalate, a polybutylene terephthalate, a polyetherimide, a polyethersulfone, and/or a polytrimethylene terephthalate.
In some embodiments, relatively elastic portions of the orthodontic appliance can be formed via direct fabrication using one or more of the following materials: a styrenic block copolymer (SBC), a silicone rubber, an elastomeric alloy, a thermoplastic elastomer (TPE), a thermoplastic vulcanizate (TPV) elastomer, a polyurethane elastomer, a block copolymer elastomer, a polyolefin blend elastomer, a thermoplastic co-polyester elastomer, and/or a thermoplastic polyamide elastomer.
Optionally, the direct fabrication methods described herein allow for fabrication of an appliance including multiple materials, referred to herein as “multi-material direct fabrication.” In some embodiments, a multi-material direct fabrication method involves concurrently forming an object from multiple materials in a single manufacturing step using the same fabrication machine and method. For instance, a multi-tip extrusion apparatus can be used to selectively dispense multiple types of materials (e.g., resins, liquids, solids, or combinations thereof) from distinct material supply sources in order to fabricate an object from a plurality of different materials. Such methods are described in U.S. Pat. No. 6,749,414, the disclosure of which is incorporated herein by reference in its entirety. Alternatively or in combination, a multi-material direct fabrication method can involve forming an object from multiple materials in a plurality of sequential manufacturing steps. For instance, a first portion of the object can be formed from a first material in accordance with any of the direct fabrication methods herein, then a second portion of the object can be formed from a second material in accordance with methods herein, and so on, until the entirety of the object has been formed.
In many embodiments, post-processing of appliances includes cleaning, post-curing, and/or support removal processes. Relevant post-processing parameters can include purity of cleaning agent, cleaning pressure and/or temperature, cleaning time, post-curing energy and/or time, and/or consistency of support removal process. These parameters can be measured and adjusted as part of a process control scheme. In addition, appliance physical properties can be varied by modifying the post-processing parameters. Adjusting post-processing machine parameters can provide another way to compensate for variability in material properties and/or machine properties.
Although various embodiments herein are described with respect to direct fabrication techniques, it shall be appreciated that other techniques can also be used, such as indirect fabrication techniques. In some embodiments, the appliances herein (or portions thereof) can be produced using indirect fabrication techniques, such as by thermoforming over a positive or negative mold. Indirect fabrication of an orthodontic appliance can involve one or more of the following steps: producing a positive or negative mold of the patient's dentition in a target arrangement (e.g., by additive manufacturing, milling, etc.), thermoforming one or more sheets of material over the mold in order to generate an appliance shell, forming one or more structures in the shell (e.g., by cutting, etching, etc.), and/or coupling one or more components to the shell (e.g., by extrusion, additive manufacturing, spraying, thermoforming, adhesives, bonding, fasteners, etc.). Optionally, one or more auxiliary appliance components as described herein (e.g., elastics, wires, springs, bars, arch expanders, palatal expanders, twin blocks, occlusal blocks, bite ramps, mandibular advancement splints, bite plates, pontics, hooks, brackets, headgear tubes, bumper tubes, palatal bars, frameworks, pin-and-tube apparatuses, buccal shields, buccinator bows, wire shields, lingual flanges and pads, lip pads or bumpers, protrusions, divots, etc.) are formed separately from and coupled to the appliance shell (e.g., via adhesives, bonding, fasteners, mounting features, etc.) after the shell has been fabricated.
The orthodontic appliances herein can be fabricated using a combination of direct and indirect fabrication techniques, such that different portions of an appliance can be fabricated using different fabrication techniques and assembled in order to form the final appliance. For example, an appliance shell can be formed by indirect fabrication (e.g., thermoforming), and one or more structures or components as described herein (e.g., auxiliary components, power arms, etc.) can be added to the shell by direct fabrication (e.g., printing onto the shell).
The configuration of the orthodontic appliances herein can be determined according to a treatment plan for a patient, e.g., a treatment plan involving successive administration of a plurality of appliances for incrementally repositioning teeth. Computer-based treatment planning and/or appliance manufacturing methods can be used in order to facilitate the design and fabrication of appliances. For instance, one or more of the appliance components described herein can be digitally designed and fabricated with the aid of computer-controlled manufacturing devices (e.g., computer numerical control (CNC) milling, computer-controlled additive manufacturing such as 3D printing, etc.). The computer-based methods presented herein can improve the accuracy, flexibility, and convenience of appliance fabrication.
The methods and apparatuses described herein may form, or be incorporated into a computer-based 3-dimensional planning/design tool, and may be used to design and fabricate the orthodontic appliances described herein.
The solver 101 may include a variety of modules, including engines, processors on which the engines may operate, and/or one or more datastores. A computer system can be implemented as an engine, as part of an engine or through multiple engines. As used herein, an engine may include one or more processors or a portion thereof. A portion of one or more processors can include some portion of hardware less than all of the hardware comprising any given one or more processors, such as a subset of registers, the portion of the processor dedicated to one or more threads of a multi-threaded processor, a time slice during which the processor is wholly or partially dedicated to carrying out part of the engine's functionality, or the like. As such, a first engine and a second engine can have one or more dedicated processors or a first engine and a second engine can share one or more processors with one another or other engines. Alternatively or additionally, different engines may share the same processor. Depending upon implementation-specific or other considerations, an engine can be centralized or its functionality distributed. An engine can include hardware, firmware, or software embodied in a computer-readable medium for execution by the processor. The processor transforms data into new data using implemented data structures and methods, such as is described with reference to the figures herein.
The engines described herein, or the engines through which the systems and devices described herein can be implemented, can be cloud-based engines. As used herein, a cloud-based engine is an engine that can run applications and/or functionalities using a cloud-based computing system. All or portions of the applications and/or functionalities can be distributed across multiple computing devices, and need not be restricted to only one computing device. In some embodiments, the cloud-based engines can execute functionalities and/or modules that end users access through a web browser or container application without having the functionalities and/or modules installed locally on the end-users' computing devices.
As used herein, datastores are intended to include repositories having any applicable organization of data, including tables, comma-separated values (CSV) files, traditional databases (e.g., SQL), or other applicable known or convenient organizational formats. Datastores can be implemented, for example, as software embodied in a physical computer-readable medium on a specific-purpose machine, in firmware, in hardware, in a combination thereof, or in an applicable known or convenient device or system. Datastore-associated components, such as database interfaces, can be considered “part of” a datastore, part of some other system component, or a combination thereof, though the physical location and other characteristics of datastore-associated components is not critical for an understanding of the techniques described herein.
Datastores can include data structures. As used herein, a data structure is associated with a particular way of storing and organizing data in a computer so that it can be used efficiently within a given context. Data structures are generally based on the ability of a computer to fetch and store data at any place in its memory, specified by an address, a bit string that can be itself stored in memory and manipulated by the program. Thus, some data structures are based on computing the addresses of data items with arithmetic operations; while other data structures are based on storing addresses of data items within the structure itself. Many data structures use both principles, sometimes combined in non-trivial ways. The implementation of a data structure usually entails writing a set of procedures that create and manipulate instances of that structure. The datastores, described herein, can be cloud-based datastores. A cloud-based datastore is a datastore that is compatible with cloud-based computing systems and engines.
In
The one or more processors 102 or any of the other elements (e.g., numeric function engine 107, numeric function building engine 109, comprehensive final position engine 111, collision detector 106, solution vector mapping engine 113, etc.) may be connected in any appropriate manner and any of these elements may also be connected to the datastores (e.g., patient teeth datastore 123, treatment targets datastore 119, treatment details datastore 117, treatment preferences datastore 115, and numeric limits datastore 121, etc.).
The numeric function building engine 109 may be used to select a plurality of numerically expressed treatment targets (e.g., from a treatment targets datastore 119 or other memory or input accessible to the one or more processors) based on the set of treatment details (which may be accessed from the treatment details datastore 117 or other accessible memory/input), the set of treatment preferences (which may be provided by the treatment preferences datastore 115 or other memory/input) and the comprehensive final position of the patient's teeth. The comprehensive final position of the patient's teeth may be provided by the comprehensive final position engine 111 or from a memory storing the comprehensive final position.
The solver may also include a numeric function minimizing engine 107 which may combine the plurality of numerically expressed treatment targets to form a single numerical function. This single numerical function may then be minimized to solve for one or more solution vectors using the collision detector 106 and a set of numeric limits that may be provided, for example, from a numeric limits datastore 121 or other memory/input. The numeric limits may be selected for the single numerical function based on the set treatment preferences (e.g., from the treatment preferences datastore 115 or other memory/input).
The solution vector typically includes all of the stages forming the treatment plan, and may be stored in a memory, and/or displayed, and/or transferred. In some variations the solution vector may be converted into a treatment plan using a solution vector mapping engine 113 to map the solution vector to a treatment plan, wherein the treatment plan includes a final tooth position that is different from the comprehensive final position of the patient's teeth.
Method of Manufacturing a Series of Aligners
In
This automated treatment planning may therefore use a treatment plan optimizing generator multiple times, each time providing slightly different treatment details and/or targets, while annotating each treatment plan with an indicator of what constraints and/or treatment targets were used to generate that treatment plan, including, for example, the fixed number of stages. The resulting multiple treatment plans may be collected into a single set (e.g., an array) and all of these treatment plans submitted back to the user via, e.g., a user interface to provide meaningful and interactive display, selection and/or manipulation of the treatment plans 315. The user (e.g., dental professional) may then, using the interactive display, in real time, toggle between the multiple plans, and select one or a subset of treatment plans 319. Optionally, the user may modify one or more plans 319; if the user modifies a treatment plan in a manner that exceed the pre-calculated plurality of treatment plans 321, then the modifications may be transmitted to back to the automated treatment planning subsystem (including the treatment plan optimizing generator) to generate additional treatment plans including the user's modifications 337. These new treatment plans may replace or supplement the plans already pre-calculated.
Optionally, once a subset of treatment plans has been selected from the larger array of treatment plans, the user may present the subset of treatment plans to a subject 323. The subject may be consulted to provide an indication (e.g., by showing the final stage/teeth position) of the orthodontic effect achievable by each treatment plan. Either the user or the subject (or both) may decide which treatment plan to choose, and the selected treatment plan may be forwarded on for fabrication/manufacturing 325 as discussed above.
In operation the system may operate the user interface module 711 in conjunction with the user selectable controls 705 to allow the user to dynamically switch (toggle) between different treatment plans, which may be displayed on a screen or other display 703 of the system by showing one or more stages, including the last (final) stage (which may be represented by a digital model of the patient's teeth in this final position), and/or properties of the treatment plan, such as the number of steps/stages, the duration of treatment, the duration of stages, the rates of tooth movements, the movement of the teeth over time (e.g., by animation or still presentation), etc. The system may display images of the teeth at the final stage for each treatment plan of a subset of the treatment plans from the array of treatment plans on the screen. A treatment properties switch 709 module may provide real-time (or near real time) switching between images of the different treatment plans within the array of treatment plans, including switching between images of the various based on one or more user-selected controls on the screen.
The system may also include a communications module 713 (e.g., wireless module, such as Wi-Fi, Bluetooth, etc.). The communications module may allow the system to receive inputs and send outputs, such as, e.g., transmitting a selected one of the treatment plans for fabrication after the user has chosen the selected one of the treatment plans displayed on the screen.
Any of these systems may also be configured to allow the user to modify one or more of the treatment plans during the display, including modifying tooth position staging timing, etc. In
In
In some variations the user interface may be configured to display a modified image of the patient's smile (e.g., the patient's teeth in a forward-facing image of the patient's face) at the conclusion of (or at any stage of) a treatment plan.
As mentioned, in some variations a specific output (including a specific user interace) for presenting one or more treatment plans to a patient may be used.
As mentioned above, the methods and apparatuses (e.g., software, firmware, hardware or some combination of these) may be configured to include a consultation mode.
From the consultation mode, the user and/or the patient may review, in a sequential or side-by-side display, the various selected treatment plans, and may select between them. The consultation mode may also include information about the cost and/or timing of the treatment plans (including the number of stages, etc.).
In general, patient information, including dental record information, may be shown as well. For example, as a reference, the methods and apparatuses may include a display of the patient's upper and lower arches (e.g., see
As mentioned above, the array of treatment plans may typically include three or more (more preferably 12 or greater) treatment plans.
An alternative treatment plan display and modification screen is shown in
Treatment Plan Optimizing Generator
Also described herein are the methods and apparatuses for automatically creating a treatment plan to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages. These methods and apparatuses may include creating a plurality of variations of treatment plans to align a patient's teeth using a plurality of removable aligners to be worn in sequential stages. The method may be referred to herein as a method for automatically generating optimized treatment plans, and the apparatus (e.g., software, including non-transient, computer-readable medium containing program instructions for creating a treatment plan to align a patient's teeth using a plurality of removable aligners) may be referred to as a treatment plan optimizing generator.
The methods for automatically generating optimized treatment plans described herein may simultaneously optimize final position and intermediate teeth positions (e.g., staging). This may allow the apparatus to produce treatment plans having a final position that is achievable in exactly the allowed number of stages (and therefore duration of treatment) for a product corresponding to a set number (or range) of aligners.
The comprehensive treatment plans built using the methods of automatically generating optimized treatment plans described herein also incorporate an optimized or idealized treatment plan (which is referred to herein as a comprehensive treatment plan) generated without consideration of the amount of time or number of stages it may take to achieve. This may enable the method to improve orthodontic measurements that are not explicitly defined as optimization goals. Measurements that represent potential orthodontic problems may be restricted to a range between the initial positions (or values) of the patient's teeth and the positions (or values) planned in the comprehensive treatment. This ensures that partial final position does not introduce or worsen orthodontic problems unnecessarily.
As an alternative to the methods and apparatuses described herein, a treatment plan may be created by first building (manually or automatically or a combination of manually and automatically) the comprehensive treatment plan, and then segmenting the plan into a series of movement-limited stages. In this method, the number of stages depends upon the final positions of the teeth. Stages are determined by, e.g., iteratively simplifying the leading tooth movements.
The method in
Described herein are methods and apparatuses for generating orthodontic treatment plans by expressing the target treatment goals for tooth movement as numerical expressions and limiting these target treatment goals by numeric constraints corresponding to limits on the treatment. Once the treatment goals and limitations are defined numerically, the resulting numeric expression (e.g., equations) may be treated as a non-linear optimization problem and solved to generate an optimal treatment plan given the constraints and target goals. These method may result in generating treatment plans that may be referred to as “partial plans” because they are not intended to fully resolve all of the patient's clinical orthodontic conditions, but may best resolve them within the given product limits (e.g., within a limited treatment time/number of stages, etc.).
A comprehensive treatment plan is typically determined without concern for all or most of the constraints forming the boundary 1903. Thus, in
One possible solution may be to find the treatment plan within the boundary that is close to the ideal final position. In
Instead, the optimal position 1911 that both resolves the principle concern (e.g., crowding of the teeth) and results in an aesthetically pleasing result is shown in the bottom left of
In practice, the ideal fit may be found by expressing the constraints as a numeric expressions and a set of limits on these numeric expression and solving the resulting expression as an optimization problem. Specifically, the method may include identifying, for a particular patient, a set of treatment preferences and treatment details, expressing these treatment preferences and treatment constrains as a nonlinear expression, and solving the optimization problem.
In any of the methods and apparatuses described herein, it may be beneficial to have an ideal tooth position (e.g., the comprehensive final tooth position) for use in the treatment planning. However, it should be clear that this comprehensive final tooth position is not used as the actual final tooth position. Instead, the methods described herein concurrently determine both the actual final tooth position and the stages required to achieve that tooth position within the limits required by the treatment preferences and treatment details. Software for determining a comprehensive final position may be used (which may also be referred to as “FiPos” software) or the final position may be manually, or semi-manually/semi-automatically determined either digitally or manually (e.g., using a model of the patient's teeth) and digitized.
Once a comprehensive final tooth position has been identified (“Full Final Position Generation”), this final position may be used, along with the initial position, treatment preferences and treatment details, to determine the optimal treatment plan, using “optimization treatment planning.” This optimized treatment planning is described in greater detail below. The optimization treatment planning may include result in a vector description including the staging, key frames (showing movement of the teeth between stages) and a proposed final position of the teeth, which may be output (“output”) by the system.
In
Similarly, the treatment preferences may be expressed as limits on the target functions. The treatment preferences, and in some variations treatment details, initial positions and comprehensive positions may be used to select the numeric limits from a stored set of pre-defined generic constraints. Once the numeric limits and target functions have been selected (e.g., based on the set of treatment details, the set of treatment preferences and the comprehensive final position of the patient's teeth) 2015, resulting in the specialized constraints (limits) 2017 and specialized numerically expressed treatment targets (target functions) 2019, they may be expressed as a non-linear optimization problem 2021 by first combining the plurality of numerically expressed treatment targets (target functions) to form a single numerical function (single numerical merit function). Each numerically expressed treatment target may be multiplied by a scaling factor. The resulting non-linear optimization problem is a single numerical function subject to the plurality of numeric limits 2023.
Thereafter, the optimization problem may be solved using conventional techniques, such as an interior point method. Such nonlinear constrained optimization solution techniques 2025 may minimize the single numerical function subject to the plurality of numeric limits to get a solution vector including all stages forming the treatment plan 2027. The solution vector may be mapped to a treatment plan 2029, wherein this “optimized” treatment plan 2033 includes a final tooth position that is different from the comprehensive final position of the patient's teeth.
An optimized treatment plan may be identified by solving an optimization problem once the constraints on the patient's teeth (e.g., product definition/treatment details and treatment preferences) are expressed as numeric functions and limits. Non-linear constrained optimization problems can be represented by a merit function and a set of inequality constraints:
See, e.g.,
The numeric limits on the single numerical function are understood to be qualities of treatment plan that must never be violated and may be defined as inequality constraints ƒi. Constraints enforce mechanical, biomechanical, clinical and aesthetic rules, as well limits imposed by product definitions. Implemented constraints include, but are not limited to, amount of reproximation, maximum velocity of tooth movement, depth of inter-arch collisions, cusp-to-groove occlusion. Example of constrains also include “do-no-harm” constrains that ensure tha the movement of the teeth does not result in making the alignment worse or overcorrecting, e.g.: midline, overjet, overbite, occlusion, misalignment, spaces, rotations, etc. Other constraints may include: amount of collisions, movement velocities and separation of movements, etc.
Qualities of treatment plan that must be improved as much as possible within constraints are defined as numerically expressed treatment targets, i.e. components of merit function, ƒ0. Targets are typically features that are to be improved or modified by the treatment. Targets may include, but are not limited to, length of the resulting treatment, amount of spaces, misalignment between teeth, etc. For example, potential chief concerns may include: misalignment (x and z), de-rotation, occlusion, diastema and spaces, inter-arch collisions, etc. Other targets may include: closeness to comprehensive setup, and roundtrips. The merit function(s) are defined as a non-linear combination of target functions, weighted by pre-defined coefficients. All of the target functions may be summed (and weighted) to form a single numerical function (single numerical merit function).
For example, target functions that may be weighted, summed and minimized as described herein may include: minimal difference with ideal final position (with the target of trying to achieve an ideal final position); misalignment, e.g., by minimizing the difference between x- and z-misalignment in value final position and in ideal one; tooth to aligned to arch (e.g., minimize the angle between the x axis in a value final position and an ideal one); minimal diastema (e.g., minimize spaces between neighboring teeth); occlusion (e.g., applicable for cases with both jaws, pull corresponding cusps from one jaw to the groves from opposite jaw); round-trip (e.g., minimize mesial-distal and buccal lingual round-trips); inter-arch collisions in value position (applicable only for cases with both jaws, e.g., try to create inter-arch collision at posterior teeth as close to ideal final position as possible); inter-arch collisions during staging, etc.
In general, measurement may be a function implemented in software that can be used as target or constraint in the optimization problem. The input for every measurement may be: 3D models of all teeth (constants), six coordinates per tooth that define the tooth position relative to the jaw. The output of every measurement may be a single numerical value, such as distance in mm, angle in radians, or score from 0 to 5.
Examples of targets are shown in
Examples of constraints include tooth movement limits that typically require that the range of movements that are allowed are limited for every dental type according the product definition. These limits may be defined clinically to ensure that the proposed treatment plans are achievable in practice with the device to be used. Each product (e.g., aligner) may different set of values, which may be stored in a look-up table or other memory accessible by the processor.
Tooth movement limits may include rotation (e.g., tooth rotation along z axis); tip (e.g., tooth rotation along x axis), torque (e.g., tooth rotation along y axis); crown movement, including horizontal crown movement (e.g., translation along z axis ignored), buccal-lingual crown movement (crown center translation along x axis), mesial-distal crown movement (e.g., crown center translation along y axis); mesial-distal root apex movement (e.g., root apex translation along y axis); buccal-lingual root apex movement (e.g., root apex translation along x axis); extrusion/intrusion (e.g., tooth translation along z axis); and relative extrusion.
Collision constraints may also be used to limit collisions between teeth. Further, staging constraints may be applied to intermediate stages (e.g., key frames) to ensure that the treatment is plan is consistent and clinically predictable. Collision constraints may include inter arch collision (applicable only for cases with both jaws), which forbids deep collisions greater than, e.g., 0.05 mm in depth, on the posterior teeth and may forbid smaller or equal depth in anterior teeth in the value final position. Collision constrains may also forbid one arch collision (e.g., so that collision between neighbor teeth in value final position does not exceed a maximum of collisions in initial, ideal final or value corresponding to the tight contact).
Staging constraints may also be used. For example, stating constraints may include synchronized finish of value final positions (e.g., every tooth must complete movement at the same stage number); fixed stage constraints (e.g., every tooth starts movement at stage 0); “not greater stage” (e.g., the final stage number, treatment length, must not be greater than allowed in the product (i.e. 20 stages, etc.); a “do not trigger Stairs pattern” (e.g., do not exceed mesial-distal movements on every tooth that can be predictably achieved in practice without long sequential teeth movements, referred to as a stairs pattern); constraints so that no Z-rotation and Intrusion/Extrusion round-trips occur on any tooth; and velocity rules (e.g., tooth movement over a single stage, e.g., aligner, must not exceed 0.25 mm).
Other examples of constraints include “do no harm” constraints, which ensure that planned final position does not introduce or worsen orthodontic problems. In the optimized final position, the value of every measurement that corresponds to an orthodontic condition must lie within the value measured in initial position, and the value measured in the comprehensive (ideal) final position. For example, overjet may be limited (applicable only for cases with both jaws) by requiring that each jaw should have at least one incisor for each side. Overbite may be limited (applicable only for cases with both jaws); each jaw should have at least one incisor for each side. Midline may be limited (applicable only for cases with both jaws); each jaw should have more than two anteriors. Occlusion may be limited (applicable only for cases with both jaws). X- and z-misalignment may be limited (applicable for each pair of neighbor teeth where at least one tooth is movable); arch and jaw occlusal plane may be calculated once in initial position. Spacing (applicable for each pair of neighbor teeth where at least one tooth is movable) may be limited; crown space between neighbor teeth should not exceed maximum of spaces in initial and ideal final positions. Angular may be limited by keeping teeth axes between initial and ideal final position (alignment to arch measurement for x, y and z axes).
Once the problem is stated in this manner it can be solved by any constrained optimization algorithm of sufficient power, such as an Interior Point method. The result is a solution vector. The vector will include position and orientation values for each tooth, as well as stage number corresponding to each tooth. The vector may describe a large number of such values, e.g., xj variables.
The produced solution vector of optimal values of xj variables may be converted to the treatment plan by the mapping of variables described above in reference to
In general, these methods may be used to generate partial treatment plans that are characterized by addressing patient's concerns as much as possible within the product limits. In contrast to comprehensive, or full, treatment plans that have as their end point the ideal, comprehensive tooth position, these partial treatment plans may not fully resolve all of the concerns of the dental professional, and may not address all orthodontic problems. To produce such partial plan, treatment length and tooth limits allowed within the product are implemented as inequality constraints. This forces the optimization algorithm to find a solution, i.e. treatment plan, within the product limits, that improves merit function as much as possible, but not to the full degree.
In general, to improve quality of the plans, an optimization target (e.g., the comprehensive tooth position) may be added to minimize distance between the partial plan and the comprehensive treatment. This distance can be measured by a length of secondary treatment that achieves comprehensive final position starting from the partial final position. Full final position for the comprehensive treatment plan may be produced manually, automatically or semi-automatically, as mentioned above, and is stored separately in apparatus. Once the partial treatment plan is ready, full final position is discarded.
To ensure that the partial treatment plans generated as described herein (optimized treatment plans) do not introduce or worsen orthodontic problems, additional inequality constraints may be introduced. As discussed above, each identified orthodontic problem, such as deep bite or class, may be measured as a single numerical value. Next, two inequalities constrain such measurement in partial setup to the range between the initial value and the comprehensive treatment. Former inequality ensures that partial setup does not worsen the problem over the initial position. The second (optional) inequality may ensure that partial setup does not overcorrect the problem unnecessarily.
By including a comprehensive final position, and incorporating it the merit function and constraints of the non-linear optimization problem, and solving this problem using the generic optimization algorithms, the method described herein may produce treatment plans that fully satisfy the constrains from the product and user preferences, while optimally resolving chief concerns, improving and maintain other orthodontic measurements.
The methods described herein can be straight-forwardly applied to all products with limits on number of stages or amount of tooth movement. By building such plans automatically, they may enable a dental professional to review multiple plans for a product range, or customize product while reviewing the updated treatment plan, as described above. Restriction on the number of stages may be replaced or supplemented with other restrictions and goals; this may allow the method to incorporate chief concerns, doctor's preferences and predictability models into comprehensive treatment plans as well.
The methods and apparatuses described herein are also fully compatible with the use of biomechanical solutions that can potentially be combined with optimization of final and intermediate tooth positions to produce treatment plans with movements that are fully supported by the appliance (e.g., aligner) design.
Collision Detection
Construction of orthodontic treatment for a patient must account for limitations on mutual position of teeth, including amount of space and interproximal reduction. Computing exact amount of collisions and spaces between teeth may be a computationally intensive operation that impacts cost and quality of automatic treatment plans. Described herein are methods and apparatuses (e.g., such as system for automatically detecting collisions between teeth, which may be referred to as collision detectors) for constructing approximated shapes of teeth by packing the surface(s) of the teeth, or in some variations, other structures (e.g., attachments, brackets, etc.) using multiple three dimensional (3D) shapes (such as capsules) having a planar figure (e.g., line, rectangle, etc.) in the core, and an outer surface extending a constant radius from the core in x, y and z. Collisions (e.g., overlap) between the teeth may be analytically determined from the 3D shapes with high precision. These systems and methods may also be applied to just adjacent portion of the teeth (rather than the entire teeth) and may be combined with a hierarchy of bounding boxes in order to accelerate the computation. Compared to precise generic technique or estimating collision, these methods and apparatuses described herein may be two or more orders of magnitude faster, and may allow the systems and methods described above for calculating one or more treatment plans (e.g., a solver or a treatment plan solver) to incorporate these collision detectors. Furthermore, any of the methods and apparatuses described herein may determine both the magnitude of the overlap (or in some cases, the closest separation) between the teeth, but may also be configured to determine the velocity of the overlap (e.g., in three or more spatial directions, such as x, y, z and/or yaw, pitch, roll).
Any appropriate 3D shape having a core that and an outer surface that is a constant radius from the core may be used, although it has been found that using shapes with at least partially linear cores (e.g., capsules, rounded rectangles, etc.) may be particularly beneficial for modeling the tooth surfaces and estimating distanced, including collisions, and depths of collision. For example,
When modeling the tooth using the 3D shapes such as capsules, a variety of different capsule sizes may be used; the capsules may have different lengths of the core line segment, and/or the radius of each capsule extending from the core may be different (though typically the same radius length in an individual capsule). Alternative or additionally, different 3D shapes may be used.
In variations in which the patient's teeth are digitally scanned, the digital scan may be segmented into individual teeth (or groups of teeth) and each segmented region, e.g., tooth, may be modeled with 3D shapes (e.g., capsules). In some variations the tooth or teeth may already be modeled in another manner (e.g., by triangles as described in
To model an individual tooth (or group of teeth), the tooth may be packed with capsules so that difference between tooth's surface and the outer surface of the capsules is as low as possible. In this model, only the 3D shapes, e.g., capsules, are used for approximations; these 3D shapes may be constructed quickly and may be analyzed quickly. As will be described in greater detail below, in some variations, only a part of a tooth may be approximated. For example, only the IP area, incisal area, crown, etc. may be modeled; for example, only the side of the tooth facing the adjacent tooth may be modeled. Alternatively, the whole tooth shape may be modeled. In some variations, the tooth shape may be decimated before approximating. For example, the tooth shape may be simplified by filtering (e.g., smoothing, etc.). Alternatively or additionally, the original shape can be used to obtain more precise approximations. In some variations, all of the teeth (or subsets of teeth) can be modeled simultaneously, e.g., in parallel. Alternatively, teeth may be modeled one-by-one, e.g., sequentially.
Modeling the tooth may be iterative. The surface may initially be filled with non-overlapping 3D shapes (e.g., capsules) as a starting configuration. Each iteration may include: finding the area of shape which is approximated by capsules the worst (e.g., comparing the digital tooth surface, or “actual surface,” to the 3D shape-filled surface). On optimization problem may be constructed for approximating this area with 1 capsule object. For example, the following relationship may be minimized, subject to the constraints that the end points of each capsule must lie inside of the convex hull of the tooth shape, and each least square straight-line (LSS) closet vertex from the tooth shape should not be farther than some small limit:
In this relationship, wk is the weight for vertex vk of the original shape. For interproximal (IP, the region between adjacent teeth) area approximations:
ωk=f({vertexk}y2)
This may be solved with an optimization solver, and a newly found surface may be added to the approximation, while obsolete surfaces may be removed from the approximation. The total approximation may then be refined. This process may be repeated (iterated) as much as necessary until the desired precision is achieved.
For example, in some variations, the systems and methods described herein may select a subset of vertices from the digital model (e.g., scan) of the tooth surface, and may approximate this subset of vertices with one capsule. An optimization solver may be used to minimize the distance between the capsule and the selected subset of points. If the approximation is poor (e.g., below some threshold for approximation), the set of point is not adequate, and the subset of vertices may be revised to find points within the vertices that may be approximated better, or finding new sets of points that may be approximated more closely within the desired range (e.g., another capsule may be identified to approximate the smaller subset of points). Once the initial packing of capsules has been completed, each approximating a small subset of points on the tooth, the optimization process may be repeated with some of the capsules rearranged to achieve a higher precisions. The process may stop when the maximum amount of capsules desired is reached. For example, the threshold number of capsules may be set to, e.g., 15 capsules (10 capsules, 12 capsules, 15 capsules, 20 capsules, 25 capsules, 30 capsules, 35 capsules, etc.). Alternatively, in some variations, the process may be repeated until a precision limit or threshold is reached, without limiting the number (and therefore size) of capsules. For example, a precision limit may be set to require filling of all spaces bigger than 0.001 mm. Alternatively, a combination or balance of the two (number of capsules and/or minimum precision limit) may be used, for example, increasing the number of capsules if the precision is within a predefined range.
Although
Once one or more surfaces of the teeth have been digitally modeled by packing 3D shapes, a hierarchy of bounding boxes may be formed around all of the 3D shapes and adjacent sets of shapes for each modeled surface of each tooth. Building a hierarchy of bounding boxes may provide a rapid and efficient way to determine which capsules between two adjacent teeth may be closest to each other and/or may overlap. The use of bounding boxes, and particularly an organized hierarchy of bounding boxes, may reduce the time for finding closest pair of capsules dramatically. The bounding boxes allow the rapid determination of an approximate value of a collision/separation in space, instead of a precise one. The approximate value may be calculated as a minimal distance between all possible pairs of capsules of two shapes. For example, approximate collisions can be used in optimization process of treatment plan generation to provide a good initial guess of teeth position that fulfills almost all requirements besides some small violations of collision/space rules left due to imprecision of approximate calculations. Those violations can be resolved by switching back to precise calculations (e.g., using the capsule distance). This combination of bounding boxes and 3D shapes, allowing both rough and more precise determination of spacing, has been found to give a substantial performance boost of up to 150 or more times compared to the use of more precise collision depth/space calculations only.
The use of the hierarchy of bounding boxes, which provide approximate collision information, with the more precise collision information provided by the 3D shapes, such as capsules, particularly by increasing the number of capsules, may allow both rapid and accurate collision/spacing information.
The hierarchy of bounding boxes may be organized so that each capsule is put into a bounding box that fits it tightly. Each bounding box containing one or more capsules are considered leafs of the hierarchy. Each box from a higher level of hierarchy bounds several boxes from a lower level of hierarchy.
Any appropriate algorithm for construction of a hierarchy of bounding boxes can be used. Using tighter bounding boxes (e.g., having smaller volumes) may result in more efficient usage of the hierarchy in collisions computations, therefore the methods and apparatuses described herein may build a hierarchy while minimizing the volume of the resulting bounding boxes on each level of hierarchy. Each tooth may have a single hierarchy, or multiple hierarchies, e.g., corresponding to the left interproximal side and the right interproximal sides, etc. The hierarchy may be traversed to avoid calculation of distances between pairs of capsules that cannot influence the outcome value of collision/space when checking adjacent teeth for collisions. For example,
In addition to detecting the magnitude of any collision that occurs when the teeth are in a specified position, the methods and apparatuses described herein may be used to determine the velocity of any collision. In doing velocity measurements, one or both teeth may be moved very small increments (e.g., less than 0.001 mm, less than 0.01 mm, etc.) in one or more axis (x, y, z axis, roll, pitch and yaw.), and the resulting change in the overlap determined for each axis. The final velocity may be measured for each of the six axes, and/or may be combined into a single indicator (e.g., vector) sum or relationship.
During any of the processes for determining the velocity of a collision, in which the tooth may be ‘jittered’ in one or more of its axes, the closest pair of capsules in collision/space with any change in position of shape is very small. This can be used for faster calculation of gradients in optimization algorithm for determining the final position of the teeth following treatment, and for staging construction. For example, the tooth may be moved in each of the six axes (e.g., three translational axes, x, y and z, and three rotational axes: pitch, yaw and roll) by a small amount (e.g., less than 0.001 mm of translation, less than 0.01 degree of rotation, etc.). This is illustrated in
In use, the method of solving for the magnitude and velocity of a collision may be integrated into a method for solving for one or more treatment plans. For example, the treatment plan solver may call on the collision detector to identify the collision and rotation between each tooth. The treatment plan solver may have initially identified digital models of the patient's teeth in which one or more surfaces were modeled by 3D figures such as capsules. See, e.g.,
A special variant of the method is used during construction of orthodontic treatment with a non-linear optimization based algorithm. For example, to compute gradients of change of collision or space amount, every step of a non-linear optimization algorithm may compute values for thousands of small variations of teeth positions. The result of previous computations may be used to select smaller number of capsules that must be considered to find the amount of collision or space, provided the change of position was limited by a small constant bound. This additional pruning reduces computational complexity from O(N2) to O(1), and allows an increase in the number of capsules used in tooth approximation without corresponding increase of computation time, increasing the precision.
In examples in which the capsules have a planar figure (e.g., line, rectangle, etc.) in the core, and an outer surface extending a constant radius from the core in x, y and z, collisions (e.g., overlap) between the teeth may be analytically determined from the 3D shapes with high precision. These systems and methods may also be applied to just adjacent portion of the teeth (rather than the entire teeth) and may be combined with a hierarchy of bounding boxes in order to accelerate the computation, as described above.
Any of the methods and apparatuses described herein, including subcomponents or subsystems (e.g., such as system or subsystem for automatically detecting collisions between teeth, which may be referred to as collision detectors) may be configured for constructing approximated shapes of teeth by packing the surface(s) of the teeth, or in some variations, other structures (e.g., attachments, brackets, etc.) using multiple three dimensional (3D) shapes (such as capsules), as described above, and these 3D shapes (“capsules”) may be selected based on the shape(s) of the product being modeled, including teeth and/or other structures, such as retainers, attachments, etc. As describes above, in general approximation of shapes using the capsules as allows a dramatic reduction in the time necessary for collision computations which may be a bottleneck in treatment plans construction. However even if the accuracy of approximation is not high, a filling (e.g., capsule-based) technique may still be useful in eliminating remaining collisions when modeling. In some variations, the construction of high-precision approximations can result in significant time savings even without requiring a refinement of treatment plans with precise computations.
For example, in some variations topographical information about the face(s) of the teeth or other targets being modeled may be used to select the size, shape and/or position of the capsules. In some variations topological information about faces (such as curvatures) is used to find regions best suitable for approximation with single capsule. This may be accomplished by identifying one or more areas on a shape having a closed curvature; in some variations, when the capsules consists of spheres of fixed radius, or shapes based on a fixed radius, they may not efficiently approximate areas that have different curvatures.
Once identified, the curvature may be used to determine the shape, size and/or position of the capsules. Capsule approximations may be refined on the fly. For example information about approximation quality in different regions may be determined, stored, and used to refine approximations interactively if collision was requested in a particular (e.g., a “bad” region, or region of high error, as shown in
For example, described herein are methods of determining/detecting collisions as described above, in which an initial partitioning of the three-dimensional shape(s) (e.g., teeth) may be based on curvatures of the outer shape surface. Thus, the method, or any apparatus configured to perform it, may identify a face of the outer surface that is close. For each face, the method or apparatus may construct a vector with coordinates (x1, . . . , x7) where x1 . . . x3 are coordinates of center of a face, x4 . . . x6 are coordinates of vector (k1*d1+k2*d2)^n (k1, k2 are principal curvatures of face, d1, d2 are principal directions, and n is normal to face), and x7 is a mean curvature of a face defined as k1+k2.
A clustering algorithm may then be used with the constructed set of vectors, and the number of clusters should be equal to desired number of capsules. In this technique, the center of each cluster may be used to construct a capsule to be used by an optimization algorithm as an initial guess. A quadric error metric may be used to control approximation sticking from 3D shape; for measuring a distance between a capsule and a plane, the following definition may be used:
Distance (Capsule,Plane)=min(quadric error metric (s(t,r),Plane))
In this technique, t belongs to [0,1], s(t) is sphere with center p0+t*(p1−p0), p0 and p1 denote end points of capsule, r is radius of capsule. This metric differs from the distance from an un-oriented point p (as opposed to a plane {p, n}) to a capsule; it also takes into account the orientation of the normals, and distinguishes naturally between convex and concave regions.
As mentioned above, any of these methods may include refinement of capsules on the fly. For example, after an approximation is computed, the apparatus or method may include marking poorly approximated areas with marker (e.g., flag). A collision computation may check if any poorly approximated areas lie near a potential collision area; if so, the method or apparatus may perform a capsule construction for the affected areas (near potential collisions; other regions with a low probability of collisions may be ignored, even if poorly approximated). Newly constructed capsules may then be added to the approximation, and the collision results may be recalculated using the newly constructed capsules.
The methods described above may be used to dramatically increase the speed and/or efficiencies of these techniques. For example,
An example of a method for generating an optimal partial treatment plan may include, for example, determining (in a computer processor) an initial position of each of the patient's teeth (position and orientation, in six variables) from a digital model of the patient's teeth. The digital model can be the upper jaw, lower jaw or both upper and lower jaws. The software typically divides the models into individual teeth and positions them into patients bite relationship. The steps of determining the position of the patient's teeth may be done in the same processor (or as part of the same device) that does the rest of the method (e.g., solves for the solution vector) or it may be a different, separate processor. Any of these methods may then determining a comprehensive (e.g., “optimal”) final position of the patient's teeth.
A processor performing the method may then receive product definition (e.g., number of stages to be used) specific to the patient treatment. Other product information may include: maximum allowed number of stages, whether attachments are allowed, maximum allowed root movements, crown movements and rotations, etc.
Thereafter the processor may receive preferences (e.g., interproximal reduction, attachments, tooth/teeth that don't move, etc.) specific to the patient treatment. Preferences may include: indicating which tooth/teeth are not to move, individual teeth where attachments should not be placed, arch to treat (both jaws, only lower or only upper), class correction amount and method, IPR, arch expansion, spaces, levelling preferences, etc.
The processor may then express a plurality of treatment targets of the treatment plan as numerical functions (“target functions”) based on the product definition and preferences, weight each numerical function, and sum them to form a single numerical function. This single numerical function may include a weighted sum of at least, for example: tooth position compared to the comprehensive final position of the patient's teeth, misalignment (x- and z-misalignment, alignment to arch), diastema (spaces between neighboring teeth), collisions (inter arch collisions), and length of treatment (number of stages). Thus, the single numerical function (e.g., the merit function) is a nonlinear combination of treatment targets (target functions) weighted by pre-defined coefficients. Typically, key components of the merit function are objective (independent of the comprehensive position) measurements of aesthetic concerns: misalignment between teeth, spacing between teeth, amount of overjet and amount of overbite. Components that are relative to comprehensive final position mostly describe orthodontic goals (arch form, occlusion, levelling, alignment, etc.).
The pre-defined coefficients may be set or determined empirically, e.g., by expert opinion, or may be solved. For example, starting from initial guess where weights are roughly same, setups may be prepared for cases from an existing database and reviewed. In addition, some adjustments can be made for technical reasons (i.e. to improve converging to a solution, which may be delayed if weights are inconsistent).
Constraints on tooth movements may then be expressed as numeric limits based on the product definition, preferences and comprehensive final position, including at least: the maximum velocity of tooth movement, maximum amount of collision, tooth movement limitations, staging constrains, and maximum amount of occlusion. As discussed above, other constraints may include: the maximum velocity of tooth movement, maximum amount of collision and space, tooth movement limitations, staging constrains, maximum amount of occlusion, amount of overbite, overjet, and midline position.
The single numerical function subject to the constraints on the tooth movements may then be solved (e.g., minimized) using a constrained optimization algorithm to get a solution vector and map the vector to a treatment plan. One example of a constrained optimization algorithm is the Interior Point method, including Interior Point method variations SQP and Active Set. Other methods may alternatively or also be used.
The solution vector is produced as a result of solving the constrained optimization algorithm. The optimization problem is defined as finding the values of variables x1 . . . xN that minimize merit function f0(x1 . . . xN) and do not violate inequality constraints fi(x1 . . . xN). Solution vector is the values of x1 . . . xN that optimization algorithm produced as an output. Variables are mapped positions teeth, for every key-frame on every tooth there are seven variables: x, y, z coordinates, angulation, inclination and rotation angles, and stage number of the key-frame. For example, x1, x2, x3, x4, x5, x6 may be the initial position of molar, x7 would be constant stage 0 (initial), then x8 . . . x14 would be position, angles and stage number of intermediate key-frame added to molar for staging, then x15 . . . x21 are final position of the molar and final stage number (length of treatment). Then x22 . . . x43 are initial, intermediate and final positions and stage numbers of pre-molar, and it continues for every tooth. There may be different number of intermediate, staging key-frames on each tooth, so 14 variables per tooth at minimum, to 42 and more variables for teeth with many staging key-frames). If multiple intermediate key-frames are present on a single tooth and their order is not fixed, each coordinate (such as angulation) of tooth at every key-frame may be calculated instead as a sum of piecewise functions parametrized by the stage number and coordinate variables. The piecewise functions may be defined so that if xi . . . xi+6 variables corresponding to six coordinates at a key-frame are equal to zero, tooth movement through this key-frame is linear, which is equivalent to absence of key-frame.
Key frames may be used to simplify the treatment plans. For example, treatment plants may be stored as positions of key frames of every tooth. A key frame is essentially an animation of teeth movement from position at initial stage, through all key-frame positions to the position at a final stage. Thus, the treatment plan does not need to store positions for every stage. Defined positions may be at initial, final, and one or more intermediate stages that are referred to as key frames. The position of tooth on a stage that is not a key frame is interpolated between the two adjacent key frames. Thus, as mentioned above, staging, i.e., intermediate positions, of each tooth may be a linear combination of several functional component. Each component describes deviation from linear movement at a certain stage and is parameterized by six coordinate deviations and a stage number.
User-Specific Treatment Preferences
The methods and apparatuses described herein typically use treatment preferences to, in part, define the target functions (and therefore merit function) and constraints that are then used to automatically pre-calculate one or more treatment plans. Each user (e.g., dental professional) may use the same general treatment preferences when treating different patients. It would be very helpful to customize treatment plan generation (and display) to the users, particularly as the same users may worth with many patients.
For example, it would be beneficial to personalize treatment planning automation for all users (e.g., dental professionals). This may be done using domain-specific language that can be integrated into the methods and apparatuses described herein. For example, the start of any treatment (including patient consultation) may include a questionnaire or template that the user completes. The treatment planning optimization engine may use a treatment template described with a domain specific language in order to control case processing flow to create treatment according to personal needs of the user.
There may be two sources of dental professional's preferences on how to prepare treatment plans. One source of treatment preferences which may be essentially a structured input where for a set of questions, the user provides answers, where each answer is a selection from a set of predefined answers. The second source of information may be represented as a text-based comments which defines the user's personal rules to follow when preparing a treatment plan for a doctor. Domain specific language may be used to store user's non-structure input (e.g., text comments describing his treatment preferences) which may enable full automation of treatment planning as well as aggregation of rules from multiple sources (for example, structured preferences and non-structured treatment preferences).
Structured treatment preferences may cover only a small portion of users' personal treatment protocols. Instead, much of the treatment protocol details may be provided by the user in non-structured, text form. While setting up a treatment plan, a technicians uses both structured treatment preferences and non-structured treatment preferences. If this information were used manually, when a technician applies text-based user preferences, misinterpretation and inconsistency in treatment plan quality may result, and the resulting treatment plan may depend on the technician. As described herein, text-based comments expressing doctors treatment planning style may be converted into a domain-specific language (manually or automatically) and the methods or apparatus (e.g., software) may interpret this domain-specific language to automatically apply doctors preferences for treatment planning preparation.
From the users perspective, the user fills two sections of his preferences describing his treatment style, e.g., on a web site. One section may be represented as questions with predefined set of answers each, and another second may be text-form comments. The user may then saves her preferences, and both types of preferences may then be applied to cases associated with (e.g., submitted by) this user.
The user's text-based preferences may be transformed into a domain-specific language which defines clinical rules to apply for treatment planning in a formal way which also may be interpreted by Treat treatment planning software. This may initially be performed manually or semi-automatically, and may initially include manual review and checking (including checking with the user). However, once the domain-specific language is constructed for that user, it may be used without requiring manual intervention, unless modified at the user's request (e.g., when displaying the resulting treatment plans, as described herein). Each user may be associated with a rules file that may be unique to the user and may be updated independently from other users.
When case is submitted by a user (e.g., requesting a treatment plan), the user's preferences, expressed in a form of a domain-specific language, may be accessed from the stored database and aggregated with other user preferences (e.g. patient-specific target preferences or additional structured input provided by the user) and may be used to execute the fully automated treatment planning described above.
Concurrently or sequentially, the method may acquire a set of scripted instructions from the user. The scripted instructions may comprise responses from a script of predefined choices (e.g., a survey, questionnaire, etc.) 2703. The responses to the set of scripted instructions may be automatically converted into a second set of rules (treatment preferences) 2707. Thereafter, the method may include accessing, by the automated treatment planning engine, the first set of treatment preferences and the second set of treatment preferences, and forming a combined set of treatment preferences from them 2709. The automated treatment planning engine may then access (e.g., receive, look-up, etc.), a digital model of the patient's teeth 2711, and any of the other inputs necessary to automatically generate a treatment plan for the patient's teeth using the combined set of treatment preferences the digital model of the patient's teeth, a comprehensive model of the patient's teeth and/or treatment details (e.g., product details), as already described above 2713.
Thus, a set of rules may be expressed in a domain specific languages and associated with each user in a clinical database. A module may converts structured input (e.g., answers given by a doctor on a set of questions) into additional set of rules. These rules may be combined via a rules aggregation module which combines rules from multiple sources into a single rules list. The language interpretation module may takes any of these rules files as an input and interpret it to control the flow of FiPos, Staging and Aligner Features modules in order to create a treatment plan fully automatically, as described above.
Automatic Selection Treatment Plans
The methods and apparatuses described herein may provide multiple treatment plans and may allow the user (e.g., the dentist, orthodontist, dental professions) and/or in some variations the patient, to view all or a subset of these treatment plans, and to select one or more of these plans from which a series of dental appliances to be manufactured treatment. As described above, a very large number (e.g., 12, 18, 24, 30, 36, 40, 48, 50, 55, 60, 65, 70, 75, 100, 125, etc.) of treatment plans may be generated concurrently. Ordering or organizing the treatment plans, and in particular, determining the order of which treatment plans to display and/or how the user may toggle or select between these different treatment plans may therefore be helpful.
In any of these variations, the treatment plans may be sorted or organized by assigning a weight to each treatment plan based one or more criterion. For example, if 24 different treatment plans are generated, it would be helpful to automatically order the treatment plans using one or more criterion and to display them in that order. For example, the treatment plans may be ordered (assigned weights) and displayed based how comprehensive they are. The degree of comprehensiveness may be based on, for example, how closely the predicted final position of the tooth resembles the ideal final position of the patient's teeth (or an arbitrary final position) that is calculated as part of the procedure for generating the multiple treatment plans described above.
In some variations, different categories of treatment plans may be displayed concurrently, e.g., the most comprehensive treatment plans among treatment plans having a first characteristic (such as a those treatment plans limited to a first number of stages, e.g., 16 stages) may be displayed alongside the most comprehensive treatment plans having a second characteristic (such as those treatment plans limited to a second number of stages, e.g., 24 stages, or unlimited stages). The methods and systems described herein may determine how comprehensive each treatment plan is by comparing to the ideal final position and/or by applying ranking logic in which the each of one or more characteristics (also referred to herein as criterion) are used to determine the weighting. For example, treatment plans with interproximal reduction (IPR) may be weighted more than plans without IPR; treatments plans with extraction may be weighted higher; treatment plans with all attachments (e.g., anterior and posterior) may be weighted higher than plans without attachments, plans with only anterior attachments may be ranked higher than those with only posterior attachments, treatment plans including both upper and lower arch may be ranked higher thank those with only one of the dental arches; upper arch only treatment plans may be ranked higher than lower arch only, etc. Each of these characteristics may provide a number of points (weights) and the final ranking may be determined by the sum of these points for each treatment plan.
In addition to, or instead of, ordering the plurality of treatment plans based on the comprehensiveness of each treatment plan, the methods and apparatuses described herein may order the treatment plans based on one or more alternative or additional criterion, such as: the duration of the treatment plan, the number of stages, the amount of tooth movement achieved, etc. The criterion may be user selected or automatically selected. In some variations, the criterion may include, for example, a prediction of a user preference; the user's preference may be determined by machine learning, and may be specific to the user (e.g., based on prior/past preferences or selections for that user) or it may be generic.
For example, in any of these variations, the system may select two of the sorted treatment plans for side-by-side (concurrent) display; in some variations along with the original tooth position and/or the ideal tooth position calculated. As mentioned, the system may select the highest-ranked treatment plans within two (or more) categories for concurrent display. The ranked treatment plans may be displayed in an initial user interface screen, from which the user may then toggle between other treatment plans using one or more controls on the user interface, as described herein. In some variations, the system selects two of the most comprehensive treatment plans and show them to the user in an initial display for user review (e.g., using a treatment review system or sub-system). The system may weight each treatment plan based on the one or more criterion. For example, the system may weights of each treatment plan based on attributes such as IPR, use of attachments (and type of attachments, and/or number of attachments, and/or where attachments are used), presence or single arch or dual arch treatment for treatment plan, etc. As mentioned above, these criterion may also be used to select categories for concurrent display. The apparatus may sort and return the most comprehensive for the case.
In practice, when generating the one or more treatment plans, the system may set up how to rank the treatment plans for initial display, e.g., in the multiple cards display. For example, the system may look at all or a subset of the parameters used when calculating the multiple different treatment plans, including but not limited to: information about IPR (e.g., IPR used/IPR not used); information about attachments set (e.g., attachments: Yes/attachments: Posteriors only/attachments: No); arches to treat (single arch treatment/dual arch treatment); treatment type (“Invisalign Go”/“Invisalign Go Plus”), etc.
In some variations, if only one Product Type is available for the Doctor, then only one Treatment Plan shall be shown in the Multiple Cards View by default. Alternatively, in some variations, the multiple cards display may be used to display single arch/both arch views or other parameters. For single arch treatments submitted by the user, the most comprehensive single arch treatment plan may be selected and shown in the Multiple Cards View for each available product type.
As mentioned above, the each of the generated treatment plans may be ranked (e.g., scored). For example, table 1 (
In the example of scoring using
From the initial view, the user may select one or more of the treatment plans for side-by-side comparison with other selected treatment plans and may begin to look though other (lower ranked) treatment plans by controlling the options/criterion. For example, the screen may include a “compare” or “save” control (e.g., button) that may allow the user to store this case for analysis. In some variations, a control may be used to move a selected treatment plan to one side of the display (e.g., in some variations replacing the initial view of the patient's teeth) so that it can be directly compared to other treatment plans.
In addition to directly toggling between options on the user interface, a control may also be provided to allow the user to see the next-ranked treatment plan (e.g., a button, or other control on the user interface, etc.). For example, selecting a “compare” button in the Multiple Cards view may be used for showing, in a dual arch treatment case, the next treatment plan according to the priorities scaling/ranking described above, such as in Table 2 of
Treatment Plan Filters
As discussed above, any of the methods and apparatuses described herein may be configured to display one or more treatment plans, typically by showing one or more of the model of the patient's dental arch(es) at one or more stages in the treatment plan, and allowing the user to toggle or switch between treatment plans by changing which parameters or constraints specified when generating the treatment plans. Thus, a user may select, in real-time, an appropriate treatment plan by using filters, toggles, or switches against the clinical parameters, such as one or more of: interproximal reduction (No IPR/No, IPR), Attachments (all attachments, No Attachments, anterior only attachments, posterior only attachments, etc.), etc. These controls may be referred to as clinical filters and the user may select the most appropriate treatment plan for the particular patient using these clinical filters to rapidly compare treatment plans. Using clinical filters may also allow the user to fine-tune a selected treatment plan. For example, in some variations the user may select another value for IPR or attachments using the filters and may then immediately submit the modified treatment plan to generate a new family of modified treatment plans that may be viewed immediately or shortly thereafter.
Thus, by toggling between treatment plans, the user may automatically and quickly browse between multiple treatment plans by choosing key features that can affects final position, such as the use and placement of attachments, IPRs, treatment of one or both arches, etc. Each filter can display one or more notification or tip if the feature is not available for a particular case.
For example,
In general the displays showing the initial malocclusion 4401 and the different treatment plans 4403 and 4405 may show a flat or static view of the teeth based on the simulated movement per the treatment plan; alternatively an animation may be used, showing tooth movement across multiple stages of treatment. In some variations, the stage shown may be the final stage (showing all movement); other stages may also be shown. In some variations the 3D model showing the tooth position may be rotated (or may rotate automatically) to show different perspectives.
As mentioned above, a filter may indicate one or more notification or tip if the feature is not available for a particular case. For example,
In general, any of these displays may also include an indication of the cost or price associated with the treatment plan. For example, one of the filters may allow the doctor to compare two different product types having different price. Higher price products may have, for example, more stages in the treatment and have a wider range for clinical conditions. Thus, in any of these examples, the doctor may use filters to provide an overview of what treatments (e.g., what treatment outcomes) may be best for the patient and which may be automatically suggested by the system, as described above, for a particular patient. In some varaitoins, the doctor can use these filters to review a particular clinical feature usage (e.g., comparing one plan with IPR to another plan without IPR, etc.) and compare results. The use of filters may also allow a user to see clinical details for the selected plan.
Filters may also be applied when reviewing a single plan in greater detail, as illustrated in
In general, the user may also switch between multiple treatment plan views and single treatment plan views. For example,
The single treatment plan display (e.g., user interface) may allow the user to review staging, features available on each stage of the treatment, and teeth position on each stage. As shown in
In any of these views, tools (e.g., on the toolbar) may be used to allow the user to review and/or modify features on any stage (attachments, IPRs, pontics, etc.), as discussed above in reference to
Any of the methods (including user interfaces) described herein may be implemented as software, hardware or firmware, and may be described as a non-transitory computer-readable storage medium storing a set of instructions capable of being executed by a processor (e.g., computer, tablet, smartphone, etc.), that when executed by the processor causes the processor to control perform any of the steps, including but not limited to: displaying, communicating with the user, analyzing, modifying parameters (including timing, frequency, intensity, etc.), determining, alerting, or the like.
When a feature or element is herein referred to as being “on” another feature or element, it can be directly on the other feature or element or intervening features and/or elements may also be present. In contrast, when a feature or element is referred to as being “directly on” another feature or element, there are no intervening features or elements present. It will also be understood that, when a feature or element is referred to as being “connected”, “attached” or “coupled” to another feature or element, it can be directly connected, attached or coupled to the other feature or element or intervening features or elements may be present. In contrast, when a feature or element is referred to as being “directly connected”, “directly attached” or “directly coupled” to another feature or element, there are no intervening features or elements present. Although described or shown with respect to one embodiment, the features and elements so described or shown can apply to other embodiments. It will also be appreciated by those of skill in the art that references to a structure or feature that is disposed “adjacent” another feature may have portions that overlap or underlie the adjacent feature.
Terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. For example, as used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items and may be abbreviated as “/”.
Spatially relative terms, such as “under”, “below”, “lower”, “over”, “upper” and the like, may be used herein for ease of description to describe one element or feature's relationship to another element(s) or feature(s) as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is inverted, elements described as “under” or “beneath” other elements or features would then be oriented “over” the other elements or features. Thus, the exemplary term “under” can encompass both an orientation of over and under. The device may be otherwise oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. Similarly, the terms “upwardly”, “downwardly”, “vertical”, “horizontal” and the like are used herein for the purpose of explanation only unless specifically indicated otherwise.
Although the terms “first” and “second” may be used herein to describe various features/elements (including steps), these features/elements should not be limited by these terms, unless the context indicates otherwise. These terms may be used to distinguish one feature/element from another feature/element. Thus, a first feature/element discussed below could be termed a second feature/element, and similarly, a second feature/element discussed below could be termed a first feature/element without departing from the teachings of the present invention.
Throughout this specification and the claims which follow, unless the context requires otherwise, the word “comprise”, and variations such as “comprises” and “comprising” means various components can be co-jointly employed in the methods and articles (e.g., compositions and apparatuses including device and methods). For example, the term “comprising” will be understood to imply the inclusion of any stated elements or steps but not the exclusion of any other elements or steps.
In general, any of the apparatuses and methods described herein should be understood to be inclusive, but all or a sub-set of the components and/or steps may alternatively be exclusive, and may be expressed as “consisting of” or alternatively “consisting essentially of” the various components, steps, sub-components or sub-steps.
As used herein in the specification and claims, including as used in the examples and unless otherwise expressly specified, all numbers may be read as if prefaced by the word “about” or “approximately,” even if the term does not expressly appear. The phrase “about” or “approximately” may be used when describing magnitude and/or position to indicate that the value and/or position described is within a reasonable expected range of values and/or positions. For example, a numeric value may have a value that is +/−0.1% of the stated value (or range of values), +/−1% of the stated value (or range of values), +/−2% of the stated value (or range of values), +/−5% of the stated value (or range of values), +/−10% of the stated value (or range of values), etc. Any numerical values given herein should also be understood to include about or approximately that value, unless the context indicates otherwise. For example, if the value “10” is disclosed, then “about 10” is also disclosed. Any numerical range recited herein is intended to include all sub-ranges subsumed therein. It is also understood that when a value is disclosed that “less than or equal to” the value, “greater than or equal to the value” and possible ranges between values are also disclosed, as appropriately understood by the skilled artisan. For example, if the value “X” is disclosed the “less than or equal to X” as well as “greater than or equal to X” (e.g., where X is a numerical value) is also disclosed. It is also understood that the throughout the application, data is provided in a number of different formats, and that this data, represents endpoints and starting points, and ranges for any combination of the data points. For example, if a particular data point “10” and a particular data point “15” are disclosed, it is understood that greater than, greater than or equal to, less than, less than or equal to, and equal to 10 and 15 are considered disclosed as well as between 10 and 15. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.
Although various illustrative embodiments are described above, any of a number of changes may be made to various embodiments without departing from the scope of the invention as described by the claims. For example, the order in which various described method steps are performed may often be changed in alternative embodiments, and in other alternative embodiments one or more method steps may be skipped altogether. Optional features of various device and system embodiments may be included in some embodiments and not in others. Therefore, the foregoing description is provided primarily for exemplary purposes and should not be interpreted to limit the scope of the invention as it is set forth in the claims.
The examples and illustrations included herein show, by way of illustration and not of limitation, specific embodiments in which the subject matter may be practiced. As mentioned, other embodiments may be utilized and derived there from, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. Such embodiments of the inventive subject matter may be referred to herein individually or collectively by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept, if more than one is, in fact, disclosed. Thus, although specific embodiments have been illustrated and described herein, any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of the above embodiments, and other embodiments not specifically described herein, will be apparent to those of skill in the art upon reviewing the above description.
This patent application claims priority to U.S. Provisional Patent Application No. 62/580,432, filed on Nov. 1, 2017 (titled “REAL-TIME, INTERACTIVE DENTAL TREATMENT PLANNING”); U.S. Provisional Patent Application No. 62/580,427, filed on Nov. 1, 2017 (titled “AUTOMATIC TREATMENT PLANNING”); and U.S. Provisional Patent Application No. 62/692,551, filed on Jun. 29, 2018 (titled “AUTOMATIC TREATMENT PLANNING”) each of which is herein incorporated by reference in its entirety.
| Number | Name | Date | Kind |
|---|---|---|---|
| 2171695 | Harper | Sep 1939 | A |
| 2194790 | Gluck | Mar 1940 | A |
| 2467432 | Kesling | Apr 1949 | A |
| 2531222 | Kesling | Nov 1950 | A |
| 3089487 | Enicks et al. | May 1963 | A |
| 3092907 | Traiger | Jun 1963 | A |
| 3178820 | Kesling | Apr 1965 | A |
| 3211143 | Grossberg | Oct 1965 | A |
| 3379193 | Monsghan | Apr 1968 | A |
| 3385291 | Martin | May 1968 | A |
| 3407500 | Kesling | Oct 1968 | A |
| 3478742 | Bohlmann | Nov 1969 | A |
| 3496936 | Gores | Feb 1970 | A |
| 3533163 | Kirschenbaum | Oct 1970 | A |
| 3556093 | Quick | Jan 1971 | A |
| 3600808 | Reeve | Aug 1971 | A |
| 3660900 | Andrews | May 1972 | A |
| 3683502 | Wallshein | Aug 1972 | A |
| 3724075 | Kesling | Apr 1973 | A |
| 3738005 | Cohen et al. | Jun 1973 | A |
| 3797115 | Silverman et al. | Mar 1974 | A |
| 3860803 | Levine | Jan 1975 | A |
| 3885310 | Northcutt | May 1975 | A |
| 3916526 | Schudy | Nov 1975 | A |
| 3922786 | Lavin | Dec 1975 | A |
| 3949477 | Cohen et al. | Apr 1976 | A |
| 3950851 | Bergersen | Apr 1976 | A |
| 3955282 | McNall | May 1976 | A |
| 3983628 | Acevedo | Oct 1976 | A |
| 4014096 | Dellinger | Mar 1977 | A |
| 4055895 | Huge | Nov 1977 | A |
| 4094068 | Schinhammer | Jun 1978 | A |
| 4117596 | Wallshein | Oct 1978 | A |
| 4129946 | Kennedy | Dec 1978 | A |
| 4134208 | Pearlman | Jan 1979 | A |
| 4139944 | Bergersen | Feb 1979 | A |
| 4179811 | Hinz | Dec 1979 | A |
| 4179812 | White | Dec 1979 | A |
| 4183141 | Dellinger | Jan 1980 | A |
| 4195046 | Kesling | Mar 1980 | A |
| 4204325 | Kaelble | May 1980 | A |
| 4253828 | Coles et al. | Mar 1981 | A |
| 4255138 | Frohn | Mar 1981 | A |
| 4299568 | Crowley | Nov 1981 | A |
| 4324546 | Heitlinger et al. | Apr 1982 | A |
| 4324547 | Arcan et al. | Apr 1982 | A |
| 4348178 | Kurz | Sep 1982 | A |
| 4368040 | Weissman | Jan 1983 | A |
| 4419992 | Chorbajian | Dec 1983 | A |
| 4433956 | Witzig | Feb 1984 | A |
| 4433960 | Garito et al. | Feb 1984 | A |
| 4439154 | Mayclin | Mar 1984 | A |
| 4449928 | von Weissenfluh | May 1984 | A |
| 4478580 | Barrut | Oct 1984 | A |
| 4500294 | Lewis | Feb 1985 | A |
| 4505672 | Kurz | Mar 1985 | A |
| 4505673 | Yoshii | Mar 1985 | A |
| 4519386 | Sullivan | May 1985 | A |
| 4523908 | Drisaldi et al. | Jun 1985 | A |
| 4526540 | Dellinger | Jul 1985 | A |
| 4553936 | Wang | Nov 1985 | A |
| 4575330 | Hull | Mar 1986 | A |
| 4575805 | Moermann et al. | Mar 1986 | A |
| 4591341 | Andrews | May 1986 | A |
| 4608021 | Barrett | Aug 1986 | A |
| 4609349 | Cain | Sep 1986 | A |
| 4611288 | Duret et al. | Sep 1986 | A |
| 4629424 | Lauks et al. | Dec 1986 | A |
| 4638145 | Sakuma et al. | Jan 1987 | A |
| 4656860 | Orthuber et al. | Apr 1987 | A |
| 4663720 | Duret et al. | May 1987 | A |
| 4664626 | Kesling | May 1987 | A |
| 4665621 | Ackerman et al. | May 1987 | A |
| 4676747 | Kesling | Jun 1987 | A |
| 4755139 | Abbatte et al. | Jul 1988 | A |
| 4757824 | Chaumet | Jul 1988 | A |
| 4763791 | Halverson et al. | Aug 1988 | A |
| 4764111 | Knierim | Aug 1988 | A |
| 4790752 | Cheslak | Dec 1988 | A |
| 4793803 | Martz | Dec 1988 | A |
| 4798534 | Breads | Jan 1989 | A |
| 4830612 | Bergersen | May 1989 | A |
| 4836778 | Baumrind et al. | Jun 1989 | A |
| 4837732 | Brandestini et al. | Jun 1989 | A |
| 4850864 | Diamond | Jul 1989 | A |
| 4850865 | Napolitano | Jul 1989 | A |
| 4856991 | Breads et al. | Aug 1989 | A |
| 4877398 | Kesling | Oct 1989 | A |
| 4880380 | Martz | Nov 1989 | A |
| 4886451 | Cetlin | Dec 1989 | A |
| 4889238 | Batchelor | Dec 1989 | A |
| 4890608 | Steer | Jan 1990 | A |
| 4932866 | Guis | Jun 1990 | A |
| 4935635 | O'Harra | Jun 1990 | A |
| 4936862 | Walker et al. | Jun 1990 | A |
| 4937928 | van der Zel | Jul 1990 | A |
| 4941826 | Loran et al. | Jul 1990 | A |
| 4952928 | Carroll et al. | Aug 1990 | A |
| 4964770 | Steinbichler et al. | Oct 1990 | A |
| 4971557 | Martin | Nov 1990 | A |
| 4975052 | Spencer et al. | Dec 1990 | A |
| 4983334 | Adell | Jan 1991 | A |
| 4997369 | Shafir | Mar 1991 | A |
| 5002485 | Aagesen | Mar 1991 | A |
| 5011405 | Lemchen | Apr 1991 | A |
| 5015183 | Fenick | May 1991 | A |
| 5017133 | Miura | May 1991 | A |
| 5018969 | Andreiko et al. | May 1991 | A |
| 5027281 | Rekow et al. | Jun 1991 | A |
| 5035613 | Breads et al. | Jul 1991 | A |
| 5037295 | Bergersen | Aug 1991 | A |
| 5055039 | Abbatte et al. | Oct 1991 | A |
| 5061839 | Matsuno et al. | Oct 1991 | A |
| 5083919 | Quachi | Jan 1992 | A |
| 5094614 | Wildman | Mar 1992 | A |
| 5100316 | Wildman | Mar 1992 | A |
| 5103838 | Yousif | Apr 1992 | A |
| 5114339 | Guis | May 1992 | A |
| 5121333 | Riley et al. | Jun 1992 | A |
| 5123425 | Shannon et al. | Jun 1992 | A |
| 5128870 | Erdman et al. | Jul 1992 | A |
| 5130064 | Smalley et al. | Jul 1992 | A |
| 5131843 | Hilgers et al. | Jul 1992 | A |
| 5131844 | Marinaccio et al. | Jul 1992 | A |
| 5139419 | Andreiko et al. | Aug 1992 | A |
| 5145364 | Martz et al. | Sep 1992 | A |
| 5176517 | Truax | Jan 1993 | A |
| 5194003 | Garay et al. | Mar 1993 | A |
| 5204670 | Stinton | Apr 1993 | A |
| 5222499 | Allen et al. | Jun 1993 | A |
| 5224049 | Mushabac | Jun 1993 | A |
| 5238404 | Andreiko | Aug 1993 | A |
| 5242304 | Truax et al. | Sep 1993 | A |
| 5245592 | Kuemmel et al. | Sep 1993 | A |
| 5273429 | Rekow et al. | Dec 1993 | A |
| 5278756 | Lemchen et al. | Jan 1994 | A |
| 5306144 | Hibst et al. | Apr 1994 | A |
| 5314335 | Fung | May 1994 | A |
| 5324186 | Bakanowski | Jun 1994 | A |
| 5328362 | Watson et al. | Jul 1994 | A |
| 5335657 | Terry et al. | Aug 1994 | A |
| 5338198 | Wu et al. | Aug 1994 | A |
| 5340309 | Robertson | Aug 1994 | A |
| 5342202 | Deshayes | Aug 1994 | A |
| 5344315 | Hanson | Sep 1994 | A |
| 5368478 | Andreiko et al. | Nov 1994 | A |
| 5372502 | Massen et al. | Dec 1994 | A |
| D354355 | Hilgers | Jan 1995 | S |
| 5382164 | Stern | Jan 1995 | A |
| 5395238 | Andreiko et al. | Mar 1995 | A |
| 5415542 | Kesling | May 1995 | A |
| 5431562 | Andreiko et al. | Jul 1995 | A |
| 5440326 | Quinn | Aug 1995 | A |
| 5440496 | Andersson et al. | Aug 1995 | A |
| 5447432 | Andreiko et al. | Sep 1995 | A |
| 5449703 | Mitra et al. | Sep 1995 | A |
| 5452219 | Dehoff et al. | Sep 1995 | A |
| 5454717 | Andreiko et al. | Oct 1995 | A |
| 5456600 | Andreiko et al. | Oct 1995 | A |
| 5474448 | Andreiko et al. | Dec 1995 | A |
| 5487662 | Kipke et al. | Jan 1996 | A |
| RE35169 | Lemchen et al. | Mar 1996 | E |
| 5499633 | Fenton | Mar 1996 | A |
| 5522725 | Jordan et al. | Jun 1996 | A |
| 5528735 | Strasnick et al. | Jun 1996 | A |
| 5533895 | Andreiko et al. | Jul 1996 | A |
| 5540732 | Testerman | Jul 1996 | A |
| 5542842 | Andreiko et al. | Aug 1996 | A |
| 5543780 | McAuley et al. | Aug 1996 | A |
| 5549476 | Stern | Aug 1996 | A |
| 5562448 | Mushabac | Oct 1996 | A |
| 5570182 | Nathel et al. | Oct 1996 | A |
| 5575655 | Darnell | Nov 1996 | A |
| 5583977 | Seidl | Dec 1996 | A |
| 5587912 | Andersson et al. | Dec 1996 | A |
| 5588098 | Chen et al. | Dec 1996 | A |
| 5605459 | Kuroda et al. | Feb 1997 | A |
| 5607305 | Andersson et al. | Mar 1997 | A |
| 5614075 | Andre | Mar 1997 | A |
| 5621648 | Crump | Apr 1997 | A |
| 5626537 | Danyo et al. | May 1997 | A |
| 5636736 | Jacobs et al. | Jun 1997 | A |
| 5645420 | Bergersen | Jul 1997 | A |
| 5645421 | Slootsky | Jul 1997 | A |
| 5651671 | Seay et al. | Jul 1997 | A |
| 5655653 | Chester | Aug 1997 | A |
| 5659420 | Wakai et al. | Aug 1997 | A |
| 5683243 | Andreiko et al. | Nov 1997 | A |
| 5683244 | Truax | Nov 1997 | A |
| 5691539 | Pfeiffer | Nov 1997 | A |
| 5692894 | Schwartz et al. | Dec 1997 | A |
| 5711665 | Adam et al. | Jan 1998 | A |
| 5711666 | Hanson | Jan 1998 | A |
| 5725376 | Poirier | Mar 1998 | A |
| 5725378 | Wang | Mar 1998 | A |
| 5730151 | Summer et al. | Mar 1998 | A |
| 5737084 | Ishihara | Apr 1998 | A |
| 5740267 | Echerer et al. | Apr 1998 | A |
| 5742700 | Yoon et al. | Apr 1998 | A |
| 5769631 | Williams | Jun 1998 | A |
| 5774425 | Ivanov et al. | Jun 1998 | A |
| 5790242 | Stern et al. | Aug 1998 | A |
| 5799100 | Clarke et al. | Aug 1998 | A |
| 5800162 | Shimodaira et al. | Sep 1998 | A |
| 5800174 | Andersson | Sep 1998 | A |
| 5813854 | Nikodem | Sep 1998 | A |
| 5816800 | Brehm et al. | Oct 1998 | A |
| 5818587 | Devaraj et al. | Oct 1998 | A |
| 5823778 | Schmitt et al. | Oct 1998 | A |
| 5848115 | Little et al. | Dec 1998 | A |
| 5857853 | van Nifterick et al. | Jan 1999 | A |
| 5866058 | Batchelder et al. | Feb 1999 | A |
| 5876199 | Bergersen | Mar 1999 | A |
| 5879158 | Doyle et al. | Mar 1999 | A |
| 5880961 | Crump | Mar 1999 | A |
| 5880962 | Andersson et al. | Mar 1999 | A |
| 5882192 | Bergersen | Mar 1999 | A |
| 5886702 | Migdal et al. | Mar 1999 | A |
| 5890896 | Padial | Apr 1999 | A |
| 5904479 | Staples | May 1999 | A |
| 5934288 | Avila et al. | Aug 1999 | A |
| 5957686 | Anthony | Sep 1999 | A |
| 5964587 | Sato | Oct 1999 | A |
| 5971754 | Sondhi et al. | Oct 1999 | A |
| 5975893 | Chishti et al. | Nov 1999 | A |
| 5975906 | Knutson | Nov 1999 | A |
| 5980246 | Ramsay et al. | Nov 1999 | A |
| 5989023 | Summer et al. | Nov 1999 | A |
| 6002706 | Staver et al. | Dec 1999 | A |
| 6018713 | Coli et al. | Jan 2000 | A |
| 6044309 | Honda | Mar 2000 | A |
| 6049743 | Baba | Apr 2000 | A |
| 6053731 | Heckenberger | Apr 2000 | A |
| 6068482 | Snow | May 2000 | A |
| 6070140 | Tran | May 2000 | A |
| 6099303 | Gibbs et al. | Aug 2000 | A |
| 6099314 | Kopelman et al. | Aug 2000 | A |
| 6102701 | Engeron | Aug 2000 | A |
| 6120287 | Chen | Sep 2000 | A |
| 6123544 | Cleary | Sep 2000 | A |
| 6152731 | Jordan et al. | Nov 2000 | A |
| 6154676 | Levine | Nov 2000 | A |
| 6183248 | Chishti et al. | Feb 2001 | B1 |
| 6183249 | Brennan et al. | Feb 2001 | B1 |
| 6186780 | Hibst et al. | Feb 2001 | B1 |
| 6190165 | Andreiko et al. | Feb 2001 | B1 |
| 6200133 | Kittelsen | Mar 2001 | B1 |
| 6201880 | Elbaum et al. | Mar 2001 | B1 |
| 6210162 | Chishti et al. | Apr 2001 | B1 |
| 6212435 | Lattner et al. | Apr 2001 | B1 |
| 6213767 | Dixon et al. | Apr 2001 | B1 |
| 6217334 | Hultgren | Apr 2001 | B1 |
| 6227850 | Chishti et al. | May 2001 | B1 |
| 6230142 | Benigno et al. | May 2001 | B1 |
| 6231338 | de Josselin de Jong et al. | May 2001 | B1 |
| 6239705 | Glen | May 2001 | B1 |
| 6243601 | Wist | Jun 2001 | B1 |
| 6263234 | Engelhardt et al. | Jul 2001 | B1 |
| 6283761 | Joao | Sep 2001 | B1 |
| 6288138 | Yamamoto | Sep 2001 | B1 |
| 6299438 | Sahagian et al. | Oct 2001 | B1 |
| 6309215 | Phan et al. | Oct 2001 | B1 |
| 6313432 | Nagata et al. | Nov 2001 | B1 |
| 6315553 | Sachdeva et al. | Nov 2001 | B1 |
| 6328745 | Ascherman | Dec 2001 | B1 |
| 6332774 | Chikami | Dec 2001 | B1 |
| 6334073 | Levine | Dec 2001 | B1 |
| 6350120 | Sachdeva et al. | Feb 2002 | B1 |
| 6364660 | Durbin et al. | Apr 2002 | B1 |
| 6382975 | Poirier | May 2002 | B1 |
| 6386878 | Pavlovskaia et al. | May 2002 | B1 |
| 6394802 | Hahn | May 2002 | B1 |
| 6402510 | Williams | Jun 2002 | B1 |
| 6402707 | Ernst | Jun 2002 | B1 |
| 6405729 | Thornton | Jun 2002 | B1 |
| 6406292 | Chishti et al. | Jun 2002 | B1 |
| 6409504 | Jones et al. | Jun 2002 | B1 |
| 6413086 | Womack | Jul 2002 | B1 |
| 6414264 | von Falkenhausen | Jul 2002 | B1 |
| 6414708 | Carmeli et al. | Jul 2002 | B1 |
| 6435871 | Inman | Aug 2002 | B1 |
| 6436058 | Krahner et al. | Aug 2002 | B1 |
| 6441354 | Seghatol et al. | Aug 2002 | B1 |
| 6450167 | David et al. | Sep 2002 | B1 |
| 6450807 | Chishti et al. | Sep 2002 | B1 |
| 6462301 | Scott et al. | Oct 2002 | B1 |
| 6470338 | Rizzo et al. | Oct 2002 | B1 |
| 6471511 | Chishti et al. | Oct 2002 | B1 |
| 6471512 | Sachdeva et al. | Oct 2002 | B1 |
| 6471970 | Fanara et al. | Oct 2002 | B1 |
| 6482002 | Jordan et al. | Nov 2002 | B2 |
| 6482298 | Bhatnagar | Nov 2002 | B1 |
| 6496814 | Busche | Dec 2002 | B1 |
| 6496816 | Thiesson et al. | Dec 2002 | B1 |
| 6499026 | Rivette et al. | Dec 2002 | B1 |
| 6499995 | Schwartz | Dec 2002 | B1 |
| 6507832 | Evans et al. | Jan 2003 | B1 |
| 6514074 | Chishti et al. | Feb 2003 | B1 |
| 6515593 | Stark et al. | Feb 2003 | B1 |
| 6516288 | Bagne | Feb 2003 | B2 |
| 6516805 | Thornton | Feb 2003 | B1 |
| 6520772 | Williams | Feb 2003 | B2 |
| 6523009 | Wilkins | Feb 2003 | B1 |
| 6523019 | Borthwick | Feb 2003 | B1 |
| 6524101 | Phan et al. | Feb 2003 | B1 |
| 6526168 | Ornes et al. | Feb 2003 | B1 |
| 6526982 | Strong | Mar 2003 | B1 |
| 6529891 | Heckerman | Mar 2003 | B1 |
| 6529902 | Kanevsky et al. | Mar 2003 | B1 |
| 6532455 | Martin et al. | Mar 2003 | B1 |
| 6535865 | Skaaning et al. | Mar 2003 | B1 |
| 6540512 | Sachdeva et al. | Apr 2003 | B1 |
| 6540707 | Stark et al. | Apr 2003 | B1 |
| 6542593 | Bowman Amuah | Apr 2003 | B1 |
| 6542881 | Meidan et al. | Apr 2003 | B1 |
| 6542894 | Lee et al. | Apr 2003 | B1 |
| 6542903 | Hull et al. | Apr 2003 | B2 |
| 6551243 | Bocionek et al. | Apr 2003 | B2 |
| 6554837 | Hauri et al. | Apr 2003 | B1 |
| 6556659 | Bowman Amuah | Apr 2003 | B1 |
| 6556977 | Lapointe et al. | Apr 2003 | B1 |
| 6560592 | Reid et al. | May 2003 | B1 |
| 6564209 | Dempski et al. | May 2003 | B1 |
| 6567814 | Bankier et al. | May 2003 | B1 |
| 6571227 | Agrafiotis et al. | May 2003 | B1 |
| 6572372 | Phan et al. | Jun 2003 | B1 |
| 6573998 | Cohen Sabban | Jun 2003 | B2 |
| 6574561 | Alexander et al. | Jun 2003 | B2 |
| 6578003 | Camarda et al. | Jun 2003 | B1 |
| 6580948 | Haupert et al. | Jun 2003 | B2 |
| 6587529 | Staszewski et al. | Jul 2003 | B1 |
| 6587828 | Sachdeva | Jul 2003 | B1 |
| 6592368 | Weathers | Jul 2003 | B1 |
| 6594539 | Geng | Jul 2003 | B1 |
| 6595342 | Maritzen et al. | Jul 2003 | B1 |
| 6597934 | de Jong et al. | Jul 2003 | B1 |
| 6598043 | Baclawski | Jul 2003 | B1 |
| 6599250 | Webb et al. | Jul 2003 | B2 |
| 6602070 | Miller | Aug 2003 | B2 |
| 6604527 | Palmisano | Aug 2003 | B1 |
| 6606744 | Mikurak | Aug 2003 | B1 |
| 6607382 | Kuo et al. | Aug 2003 | B1 |
| 6611783 | Kelly et al. | Aug 2003 | B2 |
| 6611867 | Bowman Amuah | Aug 2003 | B1 |
| 6613001 | Dworkin | Sep 2003 | B1 |
| 6615158 | Wenzel et al. | Sep 2003 | B2 |
| 6616447 | Rizoiu et al. | Sep 2003 | B1 |
| 6616579 | Reinhold et al. | Sep 2003 | B1 |
| 6621491 | Baumrind et al. | Sep 2003 | B1 |
| 6623698 | Kuo | Sep 2003 | B2 |
| 6624752 | Klitsgaard et al. | Sep 2003 | B2 |
| 6626180 | Kittelsen et al. | Sep 2003 | B1 |
| 6626569 | Reinstein et al. | Sep 2003 | B2 |
| 6626669 | Zegarelli | Sep 2003 | B2 |
| 6633772 | Ford et al. | Oct 2003 | B2 |
| 6640128 | Vilsmeier et al. | Oct 2003 | B2 |
| 6643646 | Su et al. | Nov 2003 | B2 |
| 6647383 | August et al. | Nov 2003 | B1 |
| 6650944 | Goedeke et al. | Nov 2003 | B2 |
| 6671818 | Mikurak | Dec 2003 | B1 |
| 6675104 | Paulse et al. | Jan 2004 | B2 |
| 6678669 | Lapointe et al. | Jan 2004 | B2 |
| 6682346 | Chishti et al. | Jan 2004 | B2 |
| 6685469 | Chishti et al. | Feb 2004 | B2 |
| 6689055 | Mullen et al. | Feb 2004 | B1 |
| 6690761 | Lang et al. | Feb 2004 | B2 |
| 6691110 | Wang et al. | Feb 2004 | B2 |
| 6694234 | Lockwood et al. | Feb 2004 | B2 |
| 6697164 | Babayoff et al. | Feb 2004 | B1 |
| 6697793 | McGreevy | Feb 2004 | B2 |
| 6702765 | Robbins et al. | Mar 2004 | B2 |
| 6702804 | Ritter et al. | Mar 2004 | B1 |
| 6705863 | Phan et al. | Mar 2004 | B2 |
| 6729876 | Chishti et al. | May 2004 | B2 |
| 6733289 | Manemann et al. | May 2004 | B2 |
| 6736638 | Sachdeva et al. | May 2004 | B1 |
| 6739869 | Taub et al. | May 2004 | B1 |
| 6744932 | Rubbert et al. | Jun 2004 | B1 |
| 6749414 | Hanson et al. | Jun 2004 | B1 |
| 6769913 | Hurson | Aug 2004 | B2 |
| 6772026 | Bradbury et al. | Aug 2004 | B2 |
| 6790036 | Graham | Sep 2004 | B2 |
| 6802713 | Chishti et al. | Oct 2004 | B1 |
| 6814574 | Abolfathi et al. | Nov 2004 | B2 |
| 6830450 | Knopp et al. | Dec 2004 | B2 |
| 6832912 | Mao | Dec 2004 | B2 |
| 6832914 | Bonnet et al. | Dec 2004 | B1 |
| 6843370 | Tuneberg | Jan 2005 | B2 |
| 6845175 | Kopelman et al. | Jan 2005 | B2 |
| 6885464 | Pfeiffer et al. | Apr 2005 | B1 |
| 6890285 | Rahman et al. | May 2005 | B2 |
| 6951254 | Morrison | Oct 2005 | B2 |
| 6976841 | Osterwalder | Dec 2005 | B1 |
| 6978268 | Thomas et al. | Dec 2005 | B2 |
| 6983752 | Garabadian | Jan 2006 | B2 |
| 6984128 | Breining et al. | Jan 2006 | B2 |
| 6988893 | Haywood | Jan 2006 | B2 |
| 7016952 | Mullen et al. | Mar 2006 | B2 |
| 7020963 | Cleary et al. | Apr 2006 | B2 |
| 7036514 | Heck | May 2006 | B2 |
| 7040896 | Pavlovskaia et al. | May 2006 | B2 |
| 7106233 | Schroeder et al. | Sep 2006 | B2 |
| 7112065 | Kopelman et al. | Sep 2006 | B2 |
| 7121825 | Chishti et al. | Oct 2006 | B2 |
| 7134874 | Chishti et al. | Nov 2006 | B2 |
| 7137812 | Cleary et al. | Nov 2006 | B2 |
| 7138640 | Delgado et al. | Nov 2006 | B1 |
| 7140877 | Kaza | Nov 2006 | B2 |
| 7142312 | Quadling et al. | Nov 2006 | B2 |
| 7155373 | Jordan et al. | Dec 2006 | B2 |
| 7156655 | Sachdeva et al. | Jan 2007 | B2 |
| 7156661 | Choi et al. | Jan 2007 | B2 |
| 7166063 | Rahman et al. | Jan 2007 | B2 |
| 7184150 | Quadling et al. | Feb 2007 | B2 |
| 7191451 | Nakagawa | Mar 2007 | B2 |
| 7192273 | McSurdy | Mar 2007 | B2 |
| 7217131 | Vuillemot | May 2007 | B2 |
| 7220122 | Chishti | May 2007 | B2 |
| 7220124 | Taub et al. | May 2007 | B2 |
| 7229282 | Andreiko et al. | Jun 2007 | B2 |
| 7234937 | Sachdeva et al. | Jun 2007 | B2 |
| 7241142 | Abolfathi et al. | Jul 2007 | B2 |
| 7244230 | Duggirala et al. | Jul 2007 | B2 |
| 7245753 | Squilla et al. | Jul 2007 | B2 |
| 7257136 | Mori et al. | Aug 2007 | B2 |
| 7286954 | Kopelman et al. | Oct 2007 | B2 |
| 7292759 | Boutoussov et al. | Nov 2007 | B2 |
| 7294141 | Bergersen | Nov 2007 | B2 |
| 7302842 | Biester et al. | Dec 2007 | B2 |
| 7320592 | Chishti et al. | Jan 2008 | B2 |
| 7328706 | Barach et al. | Feb 2008 | B2 |
| 7329122 | Scott | Feb 2008 | B1 |
| 7338327 | Sticker et al. | Mar 2008 | B2 |
| D565509 | Fechner et al. | Apr 2008 | S |
| 7351116 | Dold | Apr 2008 | B2 |
| 7354270 | Abolfathi et al. | Apr 2008 | B2 |
| 7357637 | Liechtung | Apr 2008 | B2 |
| 7435083 | Chishti et al. | Oct 2008 | B2 |
| 7450231 | Johs et al. | Nov 2008 | B2 |
| 7458810 | Bergersen | Dec 2008 | B2 |
| 7460230 | Johs et al. | Dec 2008 | B2 |
| 7462076 | Walter et al. | Dec 2008 | B2 |
| 7463929 | Simmons | Dec 2008 | B2 |
| 7476100 | Kuo | Jan 2009 | B2 |
| 7500851 | Williams | Mar 2009 | B2 |
| D594413 | Palka et al. | Jun 2009 | S |
| 7543511 | Kimura et al. | Jun 2009 | B2 |
| 7544103 | Walter et al. | Jun 2009 | B2 |
| 7553157 | Abolfathi et al. | Jun 2009 | B2 |
| 7561273 | Stautmeister et al. | Jul 2009 | B2 |
| 7577284 | Wong et al. | Aug 2009 | B2 |
| 7596253 | Wong et al. | Sep 2009 | B2 |
| 7597594 | Stadler et al. | Oct 2009 | B2 |
| 7609875 | Liu et al. | Oct 2009 | B2 |
| D603796 | Sticker et al. | Nov 2009 | S |
| 7616319 | Woollam et al. | Nov 2009 | B1 |
| 7626705 | Altendorf | Dec 2009 | B2 |
| 7632216 | Rahman et al. | Dec 2009 | B2 |
| 7633625 | Woollam et al. | Dec 2009 | B1 |
| 7637262 | Bailey | Dec 2009 | B2 |
| 7637740 | Knopp | Dec 2009 | B2 |
| 7641473 | Sporbert et al. | Jan 2010 | B2 |
| 7668355 | Wong et al. | Feb 2010 | B2 |
| 7670179 | Müller | Mar 2010 | B2 |
| 7695327 | Bäuerle et al. | Apr 2010 | B2 |
| 7698068 | Babayoff | Apr 2010 | B2 |
| 7711447 | Lu et al. | May 2010 | B2 |
| 7724378 | Babayoff | May 2010 | B2 |
| D618619 | Walter | Jun 2010 | S |
| 7728848 | Petrov et al. | Jun 2010 | B2 |
| 7731508 | Borst | Jun 2010 | B2 |
| 7735217 | Borst | Jun 2010 | B2 |
| 7740476 | Rubbert et al. | Jun 2010 | B2 |
| 7744369 | Imgrund et al. | Jun 2010 | B2 |
| 7746339 | Matov et al. | Jun 2010 | B2 |
| 7780460 | Walter | Aug 2010 | B2 |
| 7787132 | Körner et al. | Aug 2010 | B2 |
| 7791810 | Powell | Sep 2010 | B2 |
| 7796243 | Choo-Smith et al. | Sep 2010 | B2 |
| 7806687 | Minagi et al. | Oct 2010 | B2 |
| 7806727 | Dold et al. | Oct 2010 | B2 |
| 7813787 | de Josselin de Jong et al. | Oct 2010 | B2 |
| 7824180 | Abolfathi et al. | Nov 2010 | B2 |
| 7828601 | Pyczak | Nov 2010 | B2 |
| 7841464 | Cinader et al. | Nov 2010 | B2 |
| 7845969 | Stadler et al. | Dec 2010 | B2 |
| 7854609 | Chen et al. | Dec 2010 | B2 |
| 7862336 | Kopelman et al. | Jan 2011 | B2 |
| 7869983 | Raby et al. | Jan 2011 | B2 |
| 7872760 | Ertl | Jan 2011 | B2 |
| 7874836 | McSurdy | Jan 2011 | B2 |
| 7874837 | Chishti et al. | Jan 2011 | B2 |
| 7874849 | Sticker et al. | Jan 2011 | B2 |
| 7878801 | Abolfathi et al. | Feb 2011 | B2 |
| 7878805 | Moss et al. | Feb 2011 | B2 |
| 7880751 | Kuo et al. | Feb 2011 | B2 |
| 7892474 | Shkolnik et al. | Feb 2011 | B2 |
| 7904308 | Arnone et al. | Mar 2011 | B2 |
| 7907280 | Johs et al. | Mar 2011 | B2 |
| 7929151 | Liang et al. | Apr 2011 | B2 |
| 7930189 | Kuo | Apr 2011 | B2 |
| 7947508 | Tricca et al. | May 2011 | B2 |
| 7959308 | Freeman et al. | Jun 2011 | B2 |
| 7963766 | Cronauer | Jun 2011 | B2 |
| 7970627 | Kuo et al. | Jun 2011 | B2 |
| 7985414 | Knaack et al. | Jul 2011 | B2 |
| 7986415 | Thiel et al. | Jul 2011 | B2 |
| 7987099 | Kuo et al. | Jul 2011 | B2 |
| 7991485 | Zakim | Aug 2011 | B2 |
| 8017891 | Nevin | Sep 2011 | B2 |
| 8026916 | Wen | Sep 2011 | B2 |
| 8027709 | Arnone et al. | Sep 2011 | B2 |
| 8029277 | Imgrund et al. | Oct 2011 | B2 |
| 8038444 | Kitching et al. | Oct 2011 | B2 |
| 8045772 | Kosuge et al. | Oct 2011 | B2 |
| 8054556 | Chen et al. | Nov 2011 | B2 |
| 8070490 | Roetzer et al. | Dec 2011 | B1 |
| 8075306 | Kitching et al. | Dec 2011 | B2 |
| 8077949 | Liang et al. | Dec 2011 | B2 |
| 8083556 | Stadler et al. | Dec 2011 | B2 |
| D652799 | Mueller | Jan 2012 | S |
| 8092215 | Stone-Collonge et al. | Jan 2012 | B2 |
| 8095383 | Arnone et al. | Jan 2012 | B2 |
| 8099268 | Kitching et al. | Jan 2012 | B2 |
| 8099305 | Kuo et al. | Jan 2012 | B2 |
| 8118592 | Tortorici | Feb 2012 | B2 |
| 8126025 | Takeda | Feb 2012 | B2 |
| 8136529 | Kelly | Mar 2012 | B2 |
| 8144954 | Quadling et al. | Mar 2012 | B2 |
| 8160334 | Thiel et al. | Apr 2012 | B2 |
| 8172569 | Matty et al. | May 2012 | B2 |
| 8197252 | Harrison | Jun 2012 | B1 |
| 8201560 | Dembro | Jun 2012 | B2 |
| 8215312 | Garabadian et al. | Jul 2012 | B2 |
| 8240018 | Walter et al. | Aug 2012 | B2 |
| 8275180 | Kuo | Sep 2012 | B2 |
| 8279450 | Oota et al. | Oct 2012 | B2 |
| 8292617 | Brandt et al. | Oct 2012 | B2 |
| 8294657 | Kim et al. | Oct 2012 | B2 |
| 8296952 | Greenberg | Oct 2012 | B2 |
| 8297286 | Smernoff | Oct 2012 | B2 |
| 8306608 | Mandelis et al. | Nov 2012 | B2 |
| 8314764 | Kim et al. | Nov 2012 | B2 |
| 8332015 | Ertl | Dec 2012 | B2 |
| 8354588 | Sticker et al. | Jan 2013 | B2 |
| 8366479 | Borst et al. | Feb 2013 | B2 |
| 8401826 | Cheng et al. | Mar 2013 | B2 |
| 8419428 | Lawrence | Apr 2013 | B2 |
| 8433083 | Abolfathi et al. | Apr 2013 | B2 |
| 8439672 | Matov et al. | May 2013 | B2 |
| 8465280 | Sachdeva et al. | Jun 2013 | B2 |
| 8477320 | Stock et al. | Jul 2013 | B2 |
| 8488113 | Thiel et al. | Jul 2013 | B2 |
| 8517726 | Kakavand et al. | Aug 2013 | B2 |
| 8520922 | Wang et al. | Aug 2013 | B2 |
| 8520925 | Duret et al. | Aug 2013 | B2 |
| 8523565 | Matty et al. | Sep 2013 | B2 |
| 8545221 | Stone-Collonge et al. | Oct 2013 | B2 |
| 8556625 | Lovely | Oct 2013 | B2 |
| 8570530 | Liang | Oct 2013 | B2 |
| 8573224 | Thornton | Nov 2013 | B2 |
| 8577212 | Thiel | Nov 2013 | B2 |
| 8601925 | Coto | Dec 2013 | B1 |
| 8639477 | Chelnokov et al. | Jan 2014 | B2 |
| 8650586 | Lee et al. | Feb 2014 | B2 |
| 8675706 | Seurin et al. | Mar 2014 | B2 |
| 8723029 | Pyczak et al. | May 2014 | B2 |
| 8738394 | Kuo | May 2014 | B2 |
| 8743923 | Geske et al. | Jun 2014 | B2 |
| 8753114 | Vuillemot | Jun 2014 | B2 |
| 8767270 | Curry et al. | Jul 2014 | B2 |
| 8768016 | Pan et al. | Jul 2014 | B2 |
| 8771149 | Rahman et al. | Jul 2014 | B2 |
| 8839476 | Adachi | Sep 2014 | B2 |
| 8843381 | Kuo et al. | Sep 2014 | B2 |
| 8856053 | Mah | Oct 2014 | B2 |
| 8870566 | Bergersen | Oct 2014 | B2 |
| 8874452 | Kuo | Oct 2014 | B2 |
| 8878905 | Fisker et al. | Nov 2014 | B2 |
| 8899976 | Chen et al. | Dec 2014 | B2 |
| 8936463 | Mason et al. | Jan 2015 | B2 |
| 8944812 | Kou | Feb 2015 | B2 |
| 8948482 | Levin | Feb 2015 | B2 |
| 8956058 | Rösch | Feb 2015 | B2 |
| 8992216 | Karazivan | Mar 2015 | B2 |
| 9004915 | Moss et al. | Apr 2015 | B2 |
| 9022792 | Sticker et al. | May 2015 | B2 |
| 9039418 | Rubbert | May 2015 | B1 |
| 9084535 | Girkin et al. | Jul 2015 | B2 |
| 9084657 | Matty et al. | Jul 2015 | B2 |
| 9108338 | Sirovskiy et al. | Aug 2015 | B2 |
| 9144512 | Wagner | Sep 2015 | B2 |
| 9192305 | Levin | Nov 2015 | B2 |
| 9204952 | Lampalzer | Dec 2015 | B2 |
| 9211166 | Kuo et al. | Dec 2015 | B2 |
| 9214014 | Levin | Dec 2015 | B2 |
| 9220580 | Borovinskih et al. | Dec 2015 | B2 |
| 9241774 | Li et al. | Jan 2016 | B2 |
| 9242118 | Brawn | Jan 2016 | B2 |
| 9261358 | Atiya et al. | Feb 2016 | B2 |
| 9277972 | Brandt et al. | Mar 2016 | B2 |
| 9336336 | Deichmann et al. | May 2016 | B2 |
| 9351810 | Moon | May 2016 | B2 |
| 9375300 | Matov et al. | Jun 2016 | B2 |
| 9403238 | Culp | Aug 2016 | B2 |
| 9408743 | Wagner | Aug 2016 | B1 |
| 9414897 | Wu et al. | Aug 2016 | B2 |
| 9433476 | Khardekar et al. | Sep 2016 | B2 |
| 9439568 | Atiya et al. | Sep 2016 | B2 |
| 9444981 | Bellis et al. | Sep 2016 | B2 |
| 9463287 | Lorberbaum et al. | Oct 2016 | B1 |
| 9492243 | Kuo | Nov 2016 | B2 |
| 9500635 | Islam | Nov 2016 | B2 |
| 9506808 | Jeon et al. | Nov 2016 | B2 |
| 9510918 | Sanchez | Dec 2016 | B2 |
| 9545331 | Ingemarsson-Matzen | Jan 2017 | B2 |
| 9566132 | Stone-Collonge et al. | Feb 2017 | B2 |
| 9584771 | Mandelis et al. | Feb 2017 | B2 |
| 9589329 | Levin | Mar 2017 | B2 |
| 9675427 | Kopelman | Jun 2017 | B2 |
| 9675430 | Verker et al. | Jun 2017 | B2 |
| 9693839 | Atiya et al. | Jul 2017 | B2 |
| 9730769 | Chen et al. | Aug 2017 | B2 |
| 9744006 | Ross | Aug 2017 | B2 |
| 9820829 | Kuo | Nov 2017 | B2 |
| 9830688 | Levin | Nov 2017 | B2 |
| 9844421 | Moss et al. | Dec 2017 | B2 |
| 9848985 | Yang et al. | Dec 2017 | B2 |
| 9861451 | Davis | Jan 2018 | B1 |
| 9936186 | Jesenko et al. | Apr 2018 | B2 |
| 10123706 | Elbaz et al. | Nov 2018 | B2 |
| 10123853 | Moss et al. | Nov 2018 | B2 |
| 10154889 | Chen et al. | Dec 2018 | B2 |
| 10159541 | Bindayel | Dec 2018 | B2 |
| 10172693 | Brandt et al. | Jan 2019 | B2 |
| 10195690 | Culp | Feb 2019 | B2 |
| 10231801 | Korytov et al. | Mar 2019 | B2 |
| 10238472 | Levin | Mar 2019 | B2 |
| 10258432 | Webber | Apr 2019 | B2 |
| 20010002310 | Chishti et al. | May 2001 | A1 |
| 20010032100 | Mahmud et al. | Oct 2001 | A1 |
| 20010038705 | Rubbert et al. | Nov 2001 | A1 |
| 20010041320 | Phan et al. | Nov 2001 | A1 |
| 20020004727 | Knaus et al. | Jan 2002 | A1 |
| 20020007284 | Schurenberg et al. | Jan 2002 | A1 |
| 20020010568 | Rubbert et al. | Jan 2002 | A1 |
| 20020015934 | Rubbert et al. | Feb 2002 | A1 |
| 20020025503 | Chapoulaud et al. | Feb 2002 | A1 |
| 20020026105 | Drazen | Feb 2002 | A1 |
| 20020028417 | Chapoulaud et al. | Mar 2002 | A1 |
| 20020035572 | Takatori et al. | Mar 2002 | A1 |
| 20020064752 | Durbin et al. | May 2002 | A1 |
| 20020064759 | Durbin et al. | May 2002 | A1 |
| 20020087551 | Hickey et al. | Jul 2002 | A1 |
| 20020107853 | Hofmann et al. | Aug 2002 | A1 |
| 20020188478 | Breeland et al. | Dec 2002 | A1 |
| 20020192617 | Phan et al. | Dec 2002 | A1 |
| 20030000927 | Kanaya et al. | Jan 2003 | A1 |
| 20030009252 | Pavlovskaia et al. | Jan 2003 | A1 |
| 20030019848 | Nicholas et al. | Jan 2003 | A1 |
| 20030021453 | Weise et al. | Jan 2003 | A1 |
| 20030035061 | Iwaki et al. | Feb 2003 | A1 |
| 20030049581 | Deluke | Mar 2003 | A1 |
| 20030057192 | Patel | Mar 2003 | A1 |
| 20030059736 | Lai et al. | Mar 2003 | A1 |
| 20030060532 | Subelka et al. | Mar 2003 | A1 |
| 20030068598 | Vallittu et al. | Apr 2003 | A1 |
| 20030095697 | Wood et al. | May 2003 | A1 |
| 20030101079 | McLaughlin | May 2003 | A1 |
| 20030103060 | Anderson et al. | Jun 2003 | A1 |
| 20030120517 | Eida et al. | Jun 2003 | A1 |
| 20030139834 | Nikolskiy et al. | Jul 2003 | A1 |
| 20030144886 | Taira | Jul 2003 | A1 |
| 20030172043 | Guyon et al. | Sep 2003 | A1 |
| 20030190575 | Hilliard | Oct 2003 | A1 |
| 20030192867 | Yamazaki et al. | Oct 2003 | A1 |
| 20030207224 | Lotte | Nov 2003 | A1 |
| 20030215764 | Kopelman et al. | Nov 2003 | A1 |
| 20030224311 | Cronauer | Dec 2003 | A1 |
| 20030224313 | Bergersen | Dec 2003 | A1 |
| 20030224314 | Bergersen | Dec 2003 | A1 |
| 20040002873 | Sachdeva | Jan 2004 | A1 |
| 20040009449 | Mah et al. | Jan 2004 | A1 |
| 20040013994 | Goldberg et al. | Jan 2004 | A1 |
| 20040019262 | Perelgut | Jan 2004 | A1 |
| 20040029078 | Marshall | Feb 2004 | A1 |
| 20040038168 | Choi et al. | Feb 2004 | A1 |
| 20040054304 | Raby | Mar 2004 | A1 |
| 20040054358 | Cox et al. | Mar 2004 | A1 |
| 20040058295 | Bergersen | Mar 2004 | A1 |
| 20040068199 | Echauz et al. | Apr 2004 | A1 |
| 20040078222 | Khan et al. | Apr 2004 | A1 |
| 20040080621 | Fisher et al. | Apr 2004 | A1 |
| 20040094165 | Cook | May 2004 | A1 |
| 20040107118 | Harnsberger et al. | Jun 2004 | A1 |
| 20040133083 | Comaniciu et al. | Jul 2004 | A1 |
| 20040152036 | Abolfathi | Aug 2004 | A1 |
| 20040158194 | Wolff et al. | Aug 2004 | A1 |
| 20040166463 | Wen et al. | Aug 2004 | A1 |
| 20040167646 | Jelonek et al. | Aug 2004 | A1 |
| 20040170941 | Phan et al. | Sep 2004 | A1 |
| 20040193036 | Zhou et al. | Sep 2004 | A1 |
| 20040197728 | Abolfathi et al. | Oct 2004 | A1 |
| 20040214128 | Sachdeva et al. | Oct 2004 | A1 |
| 20040219479 | Malin et al. | Nov 2004 | A1 |
| 20040220691 | Hofmeister et al. | Nov 2004 | A1 |
| 20040229185 | Knopp | Nov 2004 | A1 |
| 20040259049 | Kopelman et al. | Dec 2004 | A1 |
| 20050003318 | Choi et al. | Jan 2005 | A1 |
| 20050023356 | Wiklof et al. | Feb 2005 | A1 |
| 20050031196 | Moghaddam et al. | Feb 2005 | A1 |
| 20050037312 | Uchida | Feb 2005 | A1 |
| 20050038669 | Sachdeva et al. | Feb 2005 | A1 |
| 20050040551 | Biegler et al. | Feb 2005 | A1 |
| 20050042569 | Plan et al. | Feb 2005 | A1 |
| 20050042577 | Kvitrud et al. | Feb 2005 | A1 |
| 20050048433 | Hilliard | Mar 2005 | A1 |
| 20050074717 | Cleary et al. | Apr 2005 | A1 |
| 20050089822 | Geng | Apr 2005 | A1 |
| 20050100333 | Kerschbaumer et al. | May 2005 | A1 |
| 20050108052 | Omaboe | May 2005 | A1 |
| 20050131738 | Morris | Jun 2005 | A1 |
| 20050144150 | Ramamurthy et al. | Jun 2005 | A1 |
| 20050171594 | Machan et al. | Aug 2005 | A1 |
| 20050171630 | Dinauer et al. | Aug 2005 | A1 |
| 20050181333 | Karazivan et al. | Aug 2005 | A1 |
| 20050186524 | Abolfathi et al. | Aug 2005 | A1 |
| 20050186526 | Stewart et al. | Aug 2005 | A1 |
| 20050216314 | Secor | Sep 2005 | A1 |
| 20050233276 | Kopelman et al. | Oct 2005 | A1 |
| 20050239013 | Sachdeva | Oct 2005 | A1 |
| 20050244781 | Abels et al. | Nov 2005 | A1 |
| 20050244791 | Davis et al. | Nov 2005 | A1 |
| 20050271996 | Sporbert et al. | Dec 2005 | A1 |
| 20060056670 | Hamadeh | Mar 2006 | A1 |
| 20060057533 | McGann | Mar 2006 | A1 |
| 20060063135 | Mehl | Mar 2006 | A1 |
| 20060078842 | Sachdeva et al. | Apr 2006 | A1 |
| 20060084024 | Farrell | Apr 2006 | A1 |
| 20060093982 | Wen | May 2006 | A1 |
| 20060098007 | Rouet et al. | May 2006 | A1 |
| 20060099545 | Lia et al. | May 2006 | A1 |
| 20060099546 | Bergersen | May 2006 | A1 |
| 20060110698 | Robson | May 2006 | A1 |
| 20060111631 | Kelliher et al. | May 2006 | A1 |
| 20060115785 | Li et al. | Jun 2006 | A1 |
| 20060137813 | Robrecht et al. | Jun 2006 | A1 |
| 20060147872 | Andreiko | Jul 2006 | A1 |
| 20060154198 | Durbin et al. | Jul 2006 | A1 |
| 20060154207 | Kuo | Jul 2006 | A1 |
| 20060173715 | Wang | Aug 2006 | A1 |
| 20060183082 | Quadling et al. | Aug 2006 | A1 |
| 20060188834 | Hilliard | Aug 2006 | A1 |
| 20060188848 | Tricca et al. | Aug 2006 | A1 |
| 20060194163 | Tricca et al. | Aug 2006 | A1 |
| 20060199153 | Liu et al. | Sep 2006 | A1 |
| 20060204078 | Orth et al. | Sep 2006 | A1 |
| 20060223022 | Solomon | Oct 2006 | A1 |
| 20060223023 | Lai et al. | Oct 2006 | A1 |
| 20060223032 | Fried et al. | Oct 2006 | A1 |
| 20060223342 | Borst et al. | Oct 2006 | A1 |
| 20060234179 | Wen et al. | Oct 2006 | A1 |
| 20060257815 | De Dominicis | Nov 2006 | A1 |
| 20060275729 | Fornoff | Dec 2006 | A1 |
| 20060275731 | Wen et al. | Dec 2006 | A1 |
| 20060275736 | Wen et al. | Dec 2006 | A1 |
| 20060277075 | Salwan | Dec 2006 | A1 |
| 20060290693 | Zhou et al. | Dec 2006 | A1 |
| 20060292520 | Dillon et al. | Dec 2006 | A1 |
| 20070031775 | Andreiko | Feb 2007 | A1 |
| 20070046865 | Umeda et al. | Mar 2007 | A1 |
| 20070053048 | Kumar et al. | Mar 2007 | A1 |
| 20070054237 | Neuschafer | Mar 2007 | A1 |
| 20070065768 | Nadav | Mar 2007 | A1 |
| 20070087300 | Willison et al. | Apr 2007 | A1 |
| 20070087302 | Reising et al. | Apr 2007 | A1 |
| 20070106138 | Beiski et al. | May 2007 | A1 |
| 20070122592 | Anderson et al. | May 2007 | A1 |
| 20070128574 | Kuo et al. | Jun 2007 | A1 |
| 20070141525 | Cinader, Jr. | Jun 2007 | A1 |
| 20070141526 | Eisenberg et al. | Jun 2007 | A1 |
| 20070143135 | Lindquist et al. | Jun 2007 | A1 |
| 20070168152 | Matov et al. | Jul 2007 | A1 |
| 20070172112 | Paley et al. | Jul 2007 | A1 |
| 20070172291 | Yokoyama | Jul 2007 | A1 |
| 20070178420 | Keski-Nisula et al. | Aug 2007 | A1 |
| 20070183633 | Hoffmann | Aug 2007 | A1 |
| 20070184402 | Boutoussov et al. | Aug 2007 | A1 |
| 20070185732 | Hicks et al. | Aug 2007 | A1 |
| 20070192137 | Ombrellaro | Aug 2007 | A1 |
| 20070199929 | Rippl et al. | Aug 2007 | A1 |
| 20070215582 | Roeper et al. | Sep 2007 | A1 |
| 20070218422 | Ehrenfeld | Sep 2007 | A1 |
| 20070231765 | Phan et al. | Oct 2007 | A1 |
| 20070238065 | Sherwood et al. | Oct 2007 | A1 |
| 20070239488 | DeRosso | Oct 2007 | A1 |
| 20070263226 | Kurtz et al. | Nov 2007 | A1 |
| 20080013727 | Uemura | Jan 2008 | A1 |
| 20080020350 | Matov et al. | Jan 2008 | A1 |
| 20080045053 | Stadler et al. | Feb 2008 | A1 |
| 20080057461 | Cheng et al. | Mar 2008 | A1 |
| 20080057467 | Gittelson | Mar 2008 | A1 |
| 20080057479 | Grenness | Mar 2008 | A1 |
| 20080059238 | Park et al. | Mar 2008 | A1 |
| 20080090208 | Rubbert | Apr 2008 | A1 |
| 20080094389 | Rouet et al. | Apr 2008 | A1 |
| 20080113317 | Kemp et al. | May 2008 | A1 |
| 20080115791 | Heine | May 2008 | A1 |
| 20080118882 | Su | May 2008 | A1 |
| 20080118886 | Liang et al. | May 2008 | A1 |
| 20080141534 | Hilliard | Jun 2008 | A1 |
| 20080169122 | Shiraishi et al. | Jul 2008 | A1 |
| 20080171934 | Greenan et al. | Jul 2008 | A1 |
| 20080176448 | Muller et al. | Jul 2008 | A1 |
| 20080233530 | Cinader | Sep 2008 | A1 |
| 20080242144 | Dietz | Oct 2008 | A1 |
| 20080248443 | Chishti et al. | Oct 2008 | A1 |
| 20080254403 | Hilliard | Oct 2008 | A1 |
| 20080268400 | Moss et al. | Oct 2008 | A1 |
| 20080306724 | Kitching et al. | Dec 2008 | A1 |
| 20090029310 | Pumphrey et al. | Jan 2009 | A1 |
| 20090030290 | Kozuch et al. | Jan 2009 | A1 |
| 20090030347 | Cao | Jan 2009 | A1 |
| 20090040740 | Muller et al. | Feb 2009 | A1 |
| 20090061379 | Yamamoto et al. | Mar 2009 | A1 |
| 20090061381 | Durbin et al. | Mar 2009 | A1 |
| 20090075228 | Kumada et al. | Mar 2009 | A1 |
| 20090087050 | Gandyra | Apr 2009 | A1 |
| 20090098502 | Andreiko | Apr 2009 | A1 |
| 20090099445 | Burger | Apr 2009 | A1 |
| 20090103579 | Ushimaru et al. | Apr 2009 | A1 |
| 20090105523 | Kassayan et al. | Apr 2009 | A1 |
| 20090130620 | Yazdi et al. | May 2009 | A1 |
| 20090136890 | Kang et al. | May 2009 | A1 |
| 20090136893 | Zegarelli | May 2009 | A1 |
| 20090148809 | Kuo et al. | Jun 2009 | A1 |
| 20090170050 | Marcus | Jul 2009 | A1 |
| 20090181346 | Orth | Jul 2009 | A1 |
| 20090191502 | Cao et al. | Jul 2009 | A1 |
| 20090210032 | Beiski et al. | Aug 2009 | A1 |
| 20090218514 | Klunder et al. | Sep 2009 | A1 |
| 20090246726 | Chelnokov et al. | Oct 2009 | A1 |
| 20090281433 | Saadat et al. | Nov 2009 | A1 |
| 20090286195 | Sears et al. | Nov 2009 | A1 |
| 20090298017 | Boerjes et al. | Dec 2009 | A1 |
| 20090305540 | Stadler et al. | Dec 2009 | A1 |
| 20090316966 | Marshall et al. | Dec 2009 | A1 |
| 20090317757 | Lemchen | Dec 2009 | A1 |
| 20100015565 | Carrillo Gonzalez et al. | Jan 2010 | A1 |
| 20100019170 | Hart et al. | Jan 2010 | A1 |
| 20100028825 | Lemchen | Feb 2010 | A1 |
| 20100045902 | Ikeda et al. | Feb 2010 | A1 |
| 20100062394 | Jones et al. | Mar 2010 | A1 |
| 20100068676 | Mason et al. | Mar 2010 | A1 |
| 20100086890 | Kuo | Apr 2010 | A1 |
| 20100138025 | Morton et al. | Jun 2010 | A1 |
| 20100142789 | Chang et al. | Jun 2010 | A1 |
| 20100145664 | Hultgren et al. | Jun 2010 | A1 |
| 20100145898 | Malfliet et al. | Jun 2010 | A1 |
| 20100152599 | DuHamel et al. | Jun 2010 | A1 |
| 20100165275 | Tsukamoto et al. | Jul 2010 | A1 |
| 20100167225 | Kuo | Jul 2010 | A1 |
| 20100179789 | Sachdeva et al. | Jul 2010 | A1 |
| 20100193482 | Ow et al. | Aug 2010 | A1 |
| 20100196837 | Farrell | Aug 2010 | A1 |
| 20100216085 | Kopelman | Aug 2010 | A1 |
| 20100217130 | Weinlaender | Aug 2010 | A1 |
| 20100231577 | Kim et al. | Sep 2010 | A1 |
| 20100268363 | Karim et al. | Oct 2010 | A1 |
| 20100268515 | Vogt et al. | Oct 2010 | A1 |
| 20100279243 | Cinader et al. | Nov 2010 | A1 |
| 20100280798 | Pattijn | Nov 2010 | A1 |
| 20100281370 | Rohaly et al. | Nov 2010 | A1 |
| 20100303316 | Bullis et al. | Dec 2010 | A1 |
| 20100312484 | DuHamel et al. | Dec 2010 | A1 |
| 20100327461 | Co et al. | Dec 2010 | A1 |
| 20110007920 | Abolfathi et al. | Jan 2011 | A1 |
| 20110012901 | Kaplanyan | Jan 2011 | A1 |
| 20110045428 | Boltunov et al. | Feb 2011 | A1 |
| 20110056350 | Gale et al. | Mar 2011 | A1 |
| 20110065060 | Teixeira et al. | Mar 2011 | A1 |
| 20110081625 | Fuh | Apr 2011 | A1 |
| 20110091832 | Kim et al. | Apr 2011 | A1 |
| 20110102549 | Takahashi | May 2011 | A1 |
| 20110102566 | Zakian et al. | May 2011 | A1 |
| 20110104630 | Matov et al. | May 2011 | A1 |
| 20110136072 | Li et al. | Jun 2011 | A1 |
| 20110136090 | Kazemi | Jun 2011 | A1 |
| 20110143300 | Villaalba | Jun 2011 | A1 |
| 20110143673 | Landesman et al. | Jun 2011 | A1 |
| 20110159452 | Huang | Jun 2011 | A1 |
| 20110164810 | Zang et al. | Jul 2011 | A1 |
| 20110207072 | Schiemann | Aug 2011 | A1 |
| 20110212420 | Vuillemot | Sep 2011 | A1 |
| 20110220623 | Beutler | Sep 2011 | A1 |
| 20110235045 | Koerner et al. | Sep 2011 | A1 |
| 20110269092 | Kuo et al. | Nov 2011 | A1 |
| 20110316994 | Lemchen | Dec 2011 | A1 |
| 20120028210 | Hegyi et al. | Feb 2012 | A1 |
| 20120029883 | Heinz et al. | Feb 2012 | A1 |
| 20120040311 | Nilsson | Feb 2012 | A1 |
| 20120064477 | Schmitt | Mar 2012 | A1 |
| 20120081786 | Mizuyama et al. | Apr 2012 | A1 |
| 20120086681 | Kim et al. | Apr 2012 | A1 |
| 20120115107 | Adams | May 2012 | A1 |
| 20120129117 | McCance | May 2012 | A1 |
| 20120147912 | Moench et al. | Jun 2012 | A1 |
| 20120150494 | Anderson et al. | Jun 2012 | A1 |
| 20120166213 | Arnone et al. | Jun 2012 | A1 |
| 20120172678 | Logan et al. | Jul 2012 | A1 |
| 20120281293 | Gronenborn et al. | Nov 2012 | A1 |
| 20120295216 | Dykes et al. | Nov 2012 | A1 |
| 20120322025 | Ozawa et al. | Dec 2012 | A1 |
| 20130029284 | Teasdale | Jan 2013 | A1 |
| 20130081272 | Johnson et al. | Apr 2013 | A1 |
| 20130089828 | Borovinskih et al. | Apr 2013 | A1 |
| 20130095446 | Andreiko et al. | Apr 2013 | A1 |
| 20130103176 | Kopelman et al. | Apr 2013 | A1 |
| 20130110469 | Kopelman | May 2013 | A1 |
| 20130150689 | Shaw-Klein | Jun 2013 | A1 |
| 20130163627 | Seurin et al. | Jun 2013 | A1 |
| 20130201488 | Ishihara | Aug 2013 | A1 |
| 20130204599 | Matov et al. | Aug 2013 | A1 |
| 20130209952 | Kuo et al. | Aug 2013 | A1 |
| 20130235165 | Gharib et al. | Sep 2013 | A1 |
| 20130252195 | Popat | Sep 2013 | A1 |
| 20130266326 | Joseph et al. | Oct 2013 | A1 |
| 20130278396 | Kimmel | Oct 2013 | A1 |
| 20130280671 | Brawn et al. | Oct 2013 | A1 |
| 20130286174 | Urakabe | Oct 2013 | A1 |
| 20130293824 | Yoneyama et al. | Nov 2013 | A1 |
| 20130323664 | Parker | Dec 2013 | A1 |
| 20130323671 | Dillon et al. | Dec 2013 | A1 |
| 20130323674 | Hakomori et al. | Dec 2013 | A1 |
| 20130325431 | See et al. | Dec 2013 | A1 |
| 20130337412 | Kwon | Dec 2013 | A1 |
| 20140061974 | Tyler | Mar 2014 | A1 |
| 20140081091 | Abolfathi et al. | Mar 2014 | A1 |
| 20140093160 | Porikli et al. | Apr 2014 | A1 |
| 20140106289 | Kozlowski | Apr 2014 | A1 |
| 20140122027 | Andreiko et al. | May 2014 | A1 |
| 20140136222 | Arnone et al. | May 2014 | A1 |
| 20140142902 | Chelnokov et al. | May 2014 | A1 |
| 20140178829 | Kim | Jun 2014 | A1 |
| 20140265034 | Dudley | Sep 2014 | A1 |
| 20140272774 | Dillon et al. | Sep 2014 | A1 |
| 20140280376 | Kuo | Sep 2014 | A1 |
| 20140294273 | Jaisson | Oct 2014 | A1 |
| 20140313299 | Gebhardt et al. | Oct 2014 | A1 |
| 20140329194 | Sachdeva et al. | Nov 2014 | A1 |
| 20140342301 | Fleer et al. | Nov 2014 | A1 |
| 20140350354 | Stenzler et al. | Nov 2014 | A1 |
| 20140363778 | Parker | Dec 2014 | A1 |
| 20150002649 | Nowak et al. | Jan 2015 | A1 |
| 20150004553 | Li et al. | Jan 2015 | A1 |
| 20150021210 | Kesling | Jan 2015 | A1 |
| 20150079531 | Heine | Mar 2015 | A1 |
| 20150094564 | Tashman et al. | Apr 2015 | A1 |
| 20150097315 | DeSimone et al. | Apr 2015 | A1 |
| 20150097316 | DeSimone et al. | Apr 2015 | A1 |
| 20150102532 | DeSimone et al. | Apr 2015 | A1 |
| 20150132708 | Kuo | May 2015 | A1 |
| 20150140502 | Brawn et al. | May 2015 | A1 |
| 20150150501 | George et al. | Jun 2015 | A1 |
| 20150164335 | Van Der Poel et al. | Jun 2015 | A1 |
| 20150173856 | lowe et al. | Jun 2015 | A1 |
| 20150182303 | Abraham et al. | Jul 2015 | A1 |
| 20150216626 | Ranjbar | Aug 2015 | A1 |
| 20150216716 | Anitua Aldecoa | Aug 2015 | A1 |
| 20150230885 | Wucher | Aug 2015 | A1 |
| 20150238280 | Wu et al. | Aug 2015 | A1 |
| 20150238283 | Tanugula et al. | Aug 2015 | A1 |
| 20150306486 | Logan et al. | Oct 2015 | A1 |
| 20150320320 | Kopelman et al. | Nov 2015 | A1 |
| 20150320532 | Matty et al. | Nov 2015 | A1 |
| 20150325044 | Lebovitz | Nov 2015 | A1 |
| 20150338209 | Knüttel | Nov 2015 | A1 |
| 20150351638 | Amato | Dec 2015 | A1 |
| 20150374469 | Konno et al. | Dec 2015 | A1 |
| 20160000332 | Atiya et al. | Jan 2016 | A1 |
| 20160003610 | Lampert et al. | Jan 2016 | A1 |
| 20160022185 | Agarwal et al. | Jan 2016 | A1 |
| 20160042509 | Andreiko et al. | Feb 2016 | A1 |
| 20160051345 | Levin | Feb 2016 | A1 |
| 20160064898 | Atiya et al. | Mar 2016 | A1 |
| 20160067013 | Morton et al. | Mar 2016 | A1 |
| 20160081768 | Kopelman et al. | Mar 2016 | A1 |
| 20160081769 | Kimura et al. | Mar 2016 | A1 |
| 20160095668 | Kuo et al. | Apr 2016 | A1 |
| 20160100924 | Wilson et al. | Apr 2016 | A1 |
| 20160106520 | Borovinskih et al. | Apr 2016 | A1 |
| 20160120621 | Li et al. | May 2016 | A1 |
| 20160135924 | Choi et al. | May 2016 | A1 |
| 20160135925 | Mason | May 2016 | A1 |
| 20160163115 | Furst | Jun 2016 | A1 |
| 20160217708 | Levin et al. | Jul 2016 | A1 |
| 20160220105 | Durent | Aug 2016 | A1 |
| 20160220200 | Sandholm et al. | Aug 2016 | A1 |
| 20160225151 | Cocco et al. | Aug 2016 | A1 |
| 20160228213 | Tod et al. | Aug 2016 | A1 |
| 20160242871 | Morton et al. | Aug 2016 | A1 |
| 20160246936 | Kahn | Aug 2016 | A1 |
| 20160287358 | Nowak et al. | Oct 2016 | A1 |
| 20160296303 | Parker | Oct 2016 | A1 |
| 20160302885 | Matov et al. | Oct 2016 | A1 |
| 20160310235 | Derakhshan et al. | Oct 2016 | A1 |
| 20160328843 | Graham et al. | Nov 2016 | A1 |
| 20160338799 | Wu et al. | Nov 2016 | A1 |
| 20160346063 | Schulhof et al. | Dec 2016 | A1 |
| 20160367188 | Malik et al. | Dec 2016 | A1 |
| 20160367339 | Khardekar et al. | Dec 2016 | A1 |
| 20170007365 | Kopelman et al. | Jan 2017 | A1 |
| 20170007366 | Kopelman et al. | Jan 2017 | A1 |
| 20170007367 | Li et al. | Jan 2017 | A1 |
| 20170007368 | Boronkay | Jan 2017 | A1 |
| 20170020633 | Stone-Collonge et al. | Jan 2017 | A1 |
| 20170049311 | Borovinskih et al. | Feb 2017 | A1 |
| 20170049326 | Alfano et al. | Feb 2017 | A1 |
| 20170056131 | Alauddin et al. | Mar 2017 | A1 |
| 20170071705 | Kuo | Mar 2017 | A1 |
| 20170086943 | Mah | Mar 2017 | A1 |
| 20170100209 | Wen | Apr 2017 | A1 |
| 20170100212 | Sherwood et al. | Apr 2017 | A1 |
| 20170100213 | Kuo | Apr 2017 | A1 |
| 20170100214 | Wen | Apr 2017 | A1 |
| 20170105815 | Matov et al. | Apr 2017 | A1 |
| 20170135792 | Webber | May 2017 | A1 |
| 20170135793 | Webber et al. | May 2017 | A1 |
| 20170156821 | Kopelman et al. | Jun 2017 | A1 |
| 20170165032 | Webber et al. | Jun 2017 | A1 |
| 20170215739 | Miyasato | Aug 2017 | A1 |
| 20170251954 | Lotan et al. | Sep 2017 | A1 |
| 20170258555 | Kopelman | Sep 2017 | A1 |
| 20170265970 | Verker | Sep 2017 | A1 |
| 20170319054 | Miller et al. | Nov 2017 | A1 |
| 20170319296 | Webber et al. | Nov 2017 | A1 |
| 20170325690 | Salah et al. | Nov 2017 | A1 |
| 20170340411 | Akselrod | Nov 2017 | A1 |
| 20170340415 | Choi et al. | Nov 2017 | A1 |
| 20180000563 | Shanjani et al. | Jan 2018 | A1 |
| 20180000565 | Shanjani et al. | Jan 2018 | A1 |
| 20180028064 | Elbaz et al. | Feb 2018 | A1 |
| 20180028065 | Elbaz et al. | Feb 2018 | A1 |
| 20180055602 | Kopelman et al. | Mar 2018 | A1 |
| 20180071054 | Ha | Mar 2018 | A1 |
| 20180071055 | Kuo | Mar 2018 | A1 |
| 20180085059 | Lee | Mar 2018 | A1 |
| 20180096465 | Levin | Apr 2018 | A1 |
| 20180125610 | Carrier, Jr. et al. | May 2018 | A1 |
| 20180153648 | Shanjani et al. | Jun 2018 | A1 |
| 20180153649 | Wu et al. | Jun 2018 | A1 |
| 20180153733 | Kuo | Jun 2018 | A1 |
| 20180168788 | Fernie | Jun 2018 | A1 |
| 20180192877 | Atiya et al. | Jul 2018 | A1 |
| 20180228359 | Meyer et al. | Aug 2018 | A1 |
| 20180280118 | Cramer | Oct 2018 | A1 |
| 20180284727 | Cramer et al. | Oct 2018 | A1 |
| 20180318043 | Li et al. | Nov 2018 | A1 |
| 20180353264 | Riley et al. | Dec 2018 | A1 |
| 20180368944 | Sato et al. | Dec 2018 | A1 |
| 20190026599 | Salah et al. | Jan 2019 | A1 |
| 20190046296 | Kopelman et al. | Feb 2019 | A1 |
| 20190046297 | Kopelman et al. | Feb 2019 | A1 |
| 20190069975 | Cam et al. | Mar 2019 | A1 |
| 20190076216 | Moss et al. | Mar 2019 | A1 |
| 20190090983 | Webber et al. | Mar 2019 | A1 |
| Number | Date | Country |
|---|---|---|
| 517102 | Nov 1977 | AU |
| 3031677 | Nov 1977 | AU |
| 5598894 | Jun 1994 | AU |
| 1121955 | Apr 1982 | CA |
| 1655732 | Aug 2005 | CN |
| 1655733 | Aug 2005 | CN |
| 102017658 | Apr 2011 | CN |
| 103491895 | Jan 2014 | CN |
| 103889364 | Jun 2014 | CN |
| 204092220 | Jan 2015 | CN |
| 105496575 | Apr 2016 | CN |
| 105997274 | Oct 2016 | CN |
| 2749802 | May 1978 | DE |
| 3526198 | Feb 1986 | DE |
| 4207169 | Sep 1993 | DE |
| 69327661 | Jul 2000 | DE |
| 102005043627 | Mar 2007 | DE |
| 202010017014 | Mar 2011 | DE |
| 102011051443 | Jan 2013 | DE |
| 202012011899 | Jan 2013 | DE |
| 102014225457 | Jun 2016 | DE |
| 0428152 | May 1991 | EP |
| 490848 | Jun 1992 | EP |
| 541500 | May 1993 | EP |
| 714632 | May 1997 | EP |
| 774933 | Dec 2000 | EP |
| 731673 | May 2001 | EP |
| 1941843 | Jul 2008 | EP |
| 2437027 | Apr 2012 | EP |
| 2447754 | May 2012 | EP |
| 1989764 | Jul 2012 | EP |
| 2332221 | Nov 2012 | EP |
| 2596553 | Dec 2013 | EP |
| 2612300 | Feb 2015 | EP |
| 2848229 | Mar 2015 | EP |
| 463897 | Jan 1980 | ES |
| 2455066 | Apr 2014 | ES |
| 2369828 | Jun 1978 | FR |
| 2867377 | Sep 2005 | FR |
| 2930334 | Oct 2009 | FR |
| 1550777 | Aug 1979 | GB |
| 53-058191 | May 1978 | JP |
| 4028359 | Jan 1992 | JP |
| 08-508174 | Sep 1996 | JP |
| 09-19443 | Jan 1997 | JP |
| 2003245289 | Sep 2003 | JP |
| 2000339468 | Sep 2004 | JP |
| 2005527320 | Sep 2005 | JP |
| 2005527321 | Sep 2005 | JP |
| 2006043121 | Feb 2006 | JP |
| 2007151614 | Jun 2007 | JP |
| 2007260158 | Oct 2007 | JP |
| 2007537824 | Dec 2007 | JP |
| 2008067732 | Mar 2008 | JP |
| 2008523370 | Jul 2008 | JP |
| 04184427 | Nov 2008 | JP |
| 2009000412 | Jan 2009 | JP |
| 2009018173 | Jan 2009 | JP |
| 2009078133 | Apr 2009 | JP |
| 2009101386 | May 2009 | JP |
| 2009205330 | Sep 2009 | JP |
| 2010017726 | Jan 2010 | JP |
| 2011087733 | May 2011 | JP |
| 2012045143 | Mar 2012 | JP |
| 2013007645 | Jan 2013 | JP |
| 2013192865 | Sep 2013 | JP |
| 201735173 | Feb 2017 | JP |
| 10-20020062793 | Jul 2002 | KR |
| 10-2007010801 | Nov 2007 | KR |
| 10-20090065778 | Jun 2009 | KR |
| 10-1266966 | May 2013 | KR |
| 10-2016-041632 | Apr 2016 | KR |
| 10-2016-0071127 | Jun 2016 | KR |
| 10-1675089 | Nov 2016 | KR |
| 480166 | Mar 2002 | TW |
| WO91004713 | Apr 1991 | WO |
| WO9203102 | Mar 1992 | WO |
| WO94010935 | May 1994 | WO |
| WO9623452 | Aug 1996 | WO |
| WO98032394 | Jul 1998 | WO |
| WO98044865 | Oct 1998 | WO |
| WO0108592 | Feb 2001 | WO |
| WO0185047 | Nov 2001 | WO |
| WO02017776 | Mar 2002 | WO |
| WO02062252 | Aug 2002 | WO |
| WO02095475 | Nov 2002 | WO |
| WO03003932 | Jan 2003 | WO |
| WO2006096558 | Sep 2006 | WO |
| WO2006100700 | Sep 2006 | WO |
| WO2006133548 | Dec 2006 | WO |
| WO2007019709 | Feb 2007 | WO |
| WO2007071341 | Jun 2007 | WO |
| WO2007103377 | Sep 2007 | WO |
| WO2008115654 | Sep 2008 | WO |
| WO2009016645 | Feb 2009 | WO |
| WO2009085752 | Jul 2009 | WO |
| WO2009089129 | Jul 2009 | WO |
| WO2009146788 | Dec 2009 | WO |
| WO2009146789 | Dec 2009 | WO |
| WO2010059988 | May 2010 | WO |
| WO2010123892 | Oct 2010 | WO |
| WO2012007003 | Jan 2012 | WO |
| WO2012064684 | May 2012 | WO |
| WO2012074304 | Jun 2012 | WO |
| WO2012078980 | Jun 2012 | WO |
| WO2012083968 | Jun 2012 | WO |
| WO2012140021 | Oct 2012 | WO |
| WO2013058879 | Apr 2013 | WO |
| WO2014068107 | May 2014 | WO |
| WO2014091865 | Jun 2014 | WO |
| WO2014143911 | Sep 2014 | WO |
| WO2015015289 | Feb 2015 | WO |
| WO2015063032 | May 2015 | WO |
| WO2015112638 | Jul 2015 | WO |
| WO2015176004 | Nov 2015 | WO |
| WO2016004415 | Jan 2016 | WO |
| WO2016042393 | Mar 2016 | WO |
| WO2016061279 | Apr 2016 | WO |
| WO2016084066 | Jun 2016 | WO |
| WO2016099471 | Jun 2016 | WO |
| WO2016113745 | Jul 2016 | WO |
| WO2016116874 | Jul 2016 | WO |
| WO2016200177 | Dec 2016 | WO |
| WO2017006176 | Jan 2017 | WO |
| WO2017182654 | Oct 2017 | WO |
| WO2018057547 | Mar 2018 | WO |
| WO2018085718 | May 2018 | WO |
| WO2018232113 | Dec 2018 | WO |
| WO2019018784 | Jan 2019 | WO |
| Entry |
|---|
| US 8,553,966 B1, 10/2013, Alpern et al. (withdrawn) |
| Bernabe et al.; Are the lower incisors the best predictors for the unerupted canine and premolars sums? An analysis of peruvian sample; The Angle Orthodontist; 75(2); pp. 202-207; Mar. 2005. |
| Collins English Dictionary; Teeth (definition); 9 pages; retrieved from the internet (https:www.collinsdictionary.com/us/dictionary/english/teeth) on May 13, 2019. |
| Dental Monitoring; Basics: How to put the cheek retractor?; 1 page (Screenshot); retrieved from the interenet (https://www.youtube.com/watch?v=6K1HXw4Kq3c); May 27, 2016. |
| Dental Monitoring; Dental monitoring tutorial; 1 page (Screenshot); retrieved from the internet (https:www.youtube.com/watch?v=Dbe3udOf9_c); Mar. 18, 2015. |
| dictionary.com; Plural (definition); 6 pages; retrieved from the internet ( https://www.dictionary.com/browse/plural#) on May 13, 2019. |
| dictionary.com; Quadrant (definition); 6 pages; retrieved from the internet ( https://www.dictionary.com/browse/quadrant?s=t) on May 13, 2019. |
| Ecligner Selfie; Change your smile; 1 page (screenshot); retrieved from the internet (https:play.google.com/store/apps/details?id=parklict.ecligner); on Feb. 13, 2018. |
| Martinelli et al.; Prediction of lower permanent canine and premolars width by correlation methods; The Angle Orthodontist; 75(5); pp. 805-808; Sep. 2005. |
| Nourallah et al.; New regression equations for prediciting the size of unerupted canines and premolars in a contemporary population; The Angle Orthodontist; 72(3); pp. 216-221; Jun. 2002. |
| Paredes et al.; A new, accurate and fast digital method to predict unerupted tooth size; The Angle Orthodontist; 76(1); pp. 14-19; Jan. 2006. |
| Sobral De Agular et al.; The gingival crevicular fluid as a source of biomarkers to enhance efficiency of orthodontic and functional treatment of growing patients; Bio. Med. Research International; vol. 2017; pp. 1-7; Article ID 3257235; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2017. |
| Levin; U.S. Appl. No. 16/282,431 entitled “Estimating a surface texture of a tooth,” filed Feb. 2, 2019. |
| Chen et al.; U.S. Appl. No. 16/223,019 entitled “Release agent receptacle,” filed Dec. 17, 2018. |
| Farooq et al.; Relationship between tooth dimensions and malocclusion; JPMA: The Journal of the Pakistan Medical Association; 64(6); pp. 670-674; Jun. 2014. |
| Newcombe; DTAM: Dense tracking and mapping in real-time; 8 pages; retrieved from the internet (http://www.doc.ic.ac.uk/?ajd/Publications/newcombe_etal_iccv2011.pdf; on Dec. 2011. |
| ormco.com; Increasing clinical performance with 3D interactive treatment planning and patient-specific appliances; 8 pages; retrieved from the internet (http://www.konsident.com/wp-content/files_mf/1295385693http_ormco.com_index_cmsfilesystemaction_fileOrmcoPDF_whitepapers.pdf) on Feb. 27, 2019. |
| Video of DICOM to Surgical Guides; Can be viewed at <URL:https://youtu.be/47KtOmCEFQk; Published Apr. 4, 2016. |
| Sabina et al., U.S. Appl. No. 16/258,516 entitled “Diagnostic intraoral scanning” filed Jan. 25, 2019. |
| Sabina et al., U.S. Appl. No. 16/258,523 entitled “Diagnostic intraoral tracking” filed Jan. 25, 2019. |
| Sabina et al., U.S. Appl. No. 16/258,527 entitled “Diagnostic intraoral methods and apparatuses” filed Jan. 25, 2019. |
| Li et al.; U.S. Appl. No. 16/171,159 entitled “Alternative bite adjustment structures,” filed Oct. 25, 2018. |
| Culp; U.S. Appl. No. 16/236,220 entitled “Laser cutting,” filed Dec. 28, 2018. |
| Culp; U.S. Appl. No. 16/265,287 entitled “Laser cutting,” filed Feb. 1, 2019. |
| AADR. American Association for Dental Research; Summary of Activities; Los Angeles, CA; p. 195; Mar. 20-23,(year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1980. |
| Alcaniz et aL; An Advanced System for the Simulation and Planning of Orthodontic Treatments; Karl Heinz Hohne and Ron Kikinis (eds.); Visualization in Biomedical Computing, 4th Intl. Conf, VBC '96, Hamburg, Germany; Springer-Verlag; pp. 511-520; Sep. 22-25, 1996. |
| Alexander et al.; The DigiGraph Work Station Part 2 Clinical Management; J. Clin. Orthod.; pp. 402-407; (Author Manuscript); Jul. 1990. |
| Align Technology; Align technology announces new teen solution with introduction of invisalign teen with mandibular advancement; 2 pages; retrieved from the internet (http://investor.aligntech.com/static-files/eb4fa6bb-3e62-404f-b74d-32059366901b); Mar. 6, 2017. |
| Allesee Orthodontic Appliance: Important Tip About Wearing the Red White & Blue Active Clear Retainer System; Allesee Orthodontic Appliances—Pro Lab; 1 page; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date); 1998. |
| Allesee Orthodontic Appliances: DuraClearTM; Product information; 1 page; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1997. |
| Allesee Orthodontic Appliances; The Choice Is Clear: Red, White & Blue . . . The Simple, Affordable, No-Braces Treatment; ( product information for doctors); retrieved from the internet (http://ormco.com/aoa/appliancesservices/RWB/doctorhtml); 5 pages on May 19, 2003. |
| Allesee Orthodontic Appliances; The Choice Is Clear: Red, White & Blue . . . The Simple, Affordable, No-Braces Treatment; (product information), 6 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2003. |
| Allesee Orthodontic Appliances; The Choice is Clear: Red, White & Blue . . . The Simple, Affordable, No-Braces Treatment;(Patient Information); retrieved from the internet (http://ormco.com/aoa/appliancesservices/RWB/patients.html); 2 pages on May 19, 2003. |
| Allesee Orthodontic Appliances; The Red, White & Blue Way to Improve Your Smile; (information for patients), 2 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1992. |
| Allesee Orthodontic Appliances; You may be a candidate for this invisible no-braces treatment; product information for patients; 2 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2002. |
| Altschuler et al.; Analysis of 3-D Data for Comparative 3-D Serial Growth Pattern Studies of Oral-Facial Structures; AADR Abstracts, Program and Abstracts of Papers, 57th General Session, IADR Annual Session, Mar. 29, 1979-Apr. 1, 1979, New Orleans Marriot; Journal of Dental Research; vol. 58, Special Issue A, p. 221; Jan. 1979. |
| Altschuler et al.; Laser Electro-Optic System for Rapid Three-Dimensional (3D) Topographic Mapping of Surfaces; Optical Engineering; 20(6); pp. 953-961; Dec. 1981. |
| Altschuler et al.; Measuring Surfaces Space-Coded by a Laser-Projected Dot Matrix; SPIE Imaging q Applications for Automated Industrial Inspection and Assembly; vol. 182; pp. 187-191; Oct. 10, 1979. |
| Altschuler; 3D Mapping of Maxillo-Facial Prosthesis; AADR Abstract #607; 2 pages total, (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1980. |
| Alves et al.; New trends in food allergens detection: toward biosensing strategies; Critical Reviews in Food Science and Nutrition; 56(14); pp. 2304-2319; doi: 10.1080/10408398.2013.831026; Oct. 2016. |
| Andersson et al.; Clinical Results with Titanium Crowns Fabricated with Machine Duplication and Spark Erosion; Acta Odontologica Scandinavica; 47(5); pp. 279-286; Oct. 1989. |
| Andrews, The Six Keys to Optimal Occlusion Straight Wire, Chapter 3, L.A. Wells; pp. 13-24; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1989. |
| Bandodkar et al.; All-printed magnetically self-healing electrochemical devices; Science Advances; 2(11); 11 pages; e1601465; Nov. 2016. |
| Bandodkar et al.; Self-healing inks for autonomous repair of printable electrochemical devices; Advanced Electronic Materials; 1(12); 5 pages; 1500289; Dec. 2015. |
| Bandodkar et al.; Wearable biofuel cells: a review; Electroanalysis; 28(6); pp. 1188-1200; Jun. 2016. |
| Bandodkar et al.; Wearable chemical sensors: present challenges and future prospects; Acs Sensors; 1(5); pp. 464-482; May 11, 2016. |
| Barone et al.; Creation of 3D multi-body orthodontic models by using independent imaging sensors; Sensors; 13(2); pp. 2033-2050; Feb. 5, 2013. |
| Bartels et al.; An Introduction to Splines for Use in Computer Graphics and Geometric Modeling; Morgan Kaufmann Publishers; pp. 422-425 Jan. 1, 1987. |
| Baumrind et al, “Mapping the Skull in 3-D,” reprinted from J. Calif. Dent. Assoc, 48(2), 11 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) Fall Issue 1972. |
| Baumrind et al.; A Stereophotogrammetric System for the Detection of Prosthesis Loosening in Total Hip Arthroplasty; NATO Symposium on Applications of Human Biostereometrics; SPIE; vol. 166; pp. 112-123; Jul. 9-13, 1978. |
| Baumrind; A System for Cranio facial Mapping Through the Integration of Data from Stereo X-Ray Films and Stereo Photographs; an invited paper submitted to the 1975 American Society of Photogram Symposium on Close-Range Photogram Systems; University of Illinois; pp. 142-166; Aug. 26-30, 1975. |
| Baumrind; Integrated Three-Dimensional Craniofacial Mapping: Background, Principles, and Perspectives; Seminars in Orthodontics; 7(4); pp. 223-232; Dec. 2001. |
| Begole et al.; A Computer System for the Analysis of Dental Casts; The Angle Orthodontist; 51(3); pp. 252-258; Jul. 1981. |
| Bernard et al; Computerized Diagnosis in Orthodontics for Epidemiological Studies: A ProgressReport; (Abstract Only), J. Dental Res. Special Issue, vol. 67, p. 169, paper presented at International Association for Dental Research 66th General Session, Montreal Canada; Mar. 9-13, 1988. |
| Bhatia et al.; A Computer-Aided Design for Orthognathic Surgery; British Journal of Oral and Maxillofacial Surgery; 22(4); pp. 237-253; Aug. 1, 1984. |
| Biggerstaff et al.; Computerized Analysis of Occlusion in the Postcanine Dentition; American Journal of Orthodontics; 61(3); pp. 245-254; Mar. 1972. |
| Biggerstaff; Computerized Diagnostic Setups and Simulations; Angle Orthodontist; 40(I); pp. 28-36; Jan. 1970. |
| Biostar Operation & Training Manual. Great Lakes Orthodontics, Ltd. 199 Fire Tower Drive,Tonawanda, New York. 14150-5890, 20 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1990. |
| Blu et al.; Linear interpolation revitalized; IEEE Transactions on Image Processing; 13(5); pp. 710-719; May 2004. |
| Bourke, Coordinate System Transformation; 1 page; retrived from the internet (http://astronomy.swin.edu.au/' pbourke/prolection/coords) on Nov. 5, 2004; Jun. 1996. |
| Boyd et al.; Three Dimensional Diagnosis and Orthodontic Treatment of Complex Malocclusions With the Invisalipn Appliance; Seminars in Orthodontics; 7(4); pp. 274-293; Dec. 2001. |
| Brandestini et al.; Computer Machined Ceramic Inlays: In Vitro Marginal Adaptation; J. Dent. Res. Special Issue; (Abstract 305); vol. 64; p. 208; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1985. |
| Brook et al.; An Image Analysis System for the Determination of Tooth Dimensions from Study Casts: Comparison with Manual Measurements of Mesio-distal Diameter; Journal of Dental Research; 65(3); pp. 428-431; Mar. 1986. |
| Burstone et al.; Precision Adjustment of the Transpalatal Lingual Arch: Computer Arch Form Predetermination; American Journal of Orthodontics; 79(2);pp. 115-133; Feb. 1981. |
| Burstone; Dr. Charles J. Burstone on The Uses of the Computer in Orthodontic Practice (Part 1); Journal of Clinical Orthodontics; 13(7); pp. 442-453; (interview); Jul. 1979. |
| Burstone; Dr. Charles J. Burstone on The Uses of the Computer in Orthodontic Practice (Part 2); journal of Clinical Orthodontics; 13(8); pp. 539-551 (interview); Aug. 1979. |
| Cardinal Industrial Finishes; Powder Coatings; 6 pages; retrieved from the internet (http://www.cardinalpaint.com) on Aug. 25, 2000. |
| Carnaghan, An Alternative to Holograms for the Portrayal of Human Teeth; 4th Int'l. Conf. on Holographic Systems, Components and Applications; pp. 228-231; Sep. 15, 1993. |
| Chaconas et al,; The DigiGraph Work Station, Part 1, Basic Concepts; Journal of Clinical Orthodontics; 24(6); pp. 360-367; (Author Manuscript); Jun. 1990. |
| Chafetz et al.; Subsidence of the Femoral Prosthesis, A Stereophotogrammetric Evaluation; Clinical Orthopaedics and Related Research; No. 201; pp. 60-67; Dec. 1985. |
| Chiappone; Constructing the Gnathologic Setup and Positioner; Journal of Clinical Orthodontics; 14(2); pp. 121-133; Feb. 1980. |
| Chishti et al.; U.S. Appl. No. 60/050,342 entitled “Procedure for moving teeth using a seires of retainers,” filed Jun. 20, 1997. |
| CSI Computerized Scanning and Imaging Facility; What is a maximum/minimum intensity projection (MIP/MinIP); 1 page; retrived from the internet (http://csi.whoi.edu/content/what-maximumminimum-intensity-projection-mipminip); Jan. 4, 2010. |
| Cottingham; Gnathologic Clear Plastic Positioner; American Journal of Orthodontics; 55(1); pp. 23-31; Jan. 1969. |
| Crawford; CAD/CAM in the Dental Office: Does It Work?; Canadian Dental Journal; 57(2); pp. 121-123 Feb. 1991. |
| Crawford; Computers in Dentistry: Part 1: CAD/CAM: The Computer Moves Chairside, Part 2: F. Duret' A Man With a Vision, Part 3: The Computer Gives New Vision—Literally, Part 4: Bytes 'N Bites the Computer Moves From the Front Desk to the Operatory; Canadian Dental Journal; 54(9); pp. 661-666 Sep. 1988. |
| Crooks; CAD/CAM Comes to USC; USC Dentistry; pp. 14-17; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) Spring 1990. |
| Cureton; Correcting Malaligned Mandibular Incisors with Removable Retainers; Journal of Clinical Orthodontics; 30(7); pp. 390-395; Jul. 1996. |
| Curry et al.; Integrated Three-Dimensional Craniofacial Mapping at the Craniofacial Research InstrumentationLaboratory/University of the Pacific; Seminars in Orthodontics; 7(4); pp. 258-265; Dec. 2001. |
| Cutting et al.; Three-Dimensional Computer-Assisted Design of Craniofacial Surgical Procedures: Optimization and Interaction with Cephalometric and CT-Based Models; Plastic and Reconstructive Surgery; 77(6); pp. 877-885; Jun. 1986. |
| DCS Dental AG; The CAD/CAM ‘DCS Titan System’ for Production of Crowns/Bridges; DSC Production; pp. 1-7; Jan. 1992. |
| Defranco et al.; Three-Dimensional Large Displacement Analysis of Orthodontic Appliances; Journal of Biomechanics; 9(12); pp. 793-801; Jan. 1976. |
| Dental Institute University of Zurich Switzerland; Program for International Symposium on Computer Restorations: State of the Art of the CEREC-Method; 2 pages; May 1991. |
| Dentrac Corporation; Dentrac document; pp. 4-13; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1992. |
| Dent-X; Dentsim . . . Dent-x's virtual reality 3-D training simulator . . . A revolution in dental education; 6 pages; retrieved from the internet (http://www.dent-x.com/DentSim.htm); on Sep. 24, 1998. |
| Di Muzio et al.; Minimum intensity projection (MinIP); 6 pages; retrieved from the internet (https://radiopaedia.org/articles/minimum-intensity-projection-minip) on Sep. 6, 2018. |
| Doruk et al.; The role of the headgear timer in extraoral co-operation; European Journal of Orthodontics; 26; pp. 289-291; Jun. 1, 2004. |
| Doyle; Digital Dentistry; Computer Graphics World; pp. 50-52 andp. 54; Oct. 2000. |
| Dummer et al.; Computed Radiography Imaging Based on High-Density 670 nm VCSEL Arrays; International Society for Optics and Photonics; vol. 7557; p. 75570H; 7 pages; (Author Manuscript); Feb. 24, 2010. |
| Duret et al.; CAD/CAM Imaging in Dentistry; Current Opinion in Dentistry; 1 (2); pp. 150-154; Apr. 1991. |
| Duret et al; CAD-CAM in Dentistry; Journal of the American Dental Association; 117(6); pp. 715-720; Nov. 1988. |
| Duret; The Dental CAD/CAM, General Description of the Project; Hennson International Product Brochure, 18 pages; Jan. 1986. |
| Duret; Vers Une Prosthese Informatisee; Tonus; 75(15); pp. 55-57; (English translation attached); 23 pages; Nov. 15, 1985. |
| Economides; The Microcomputer in the Orthodontic Office; Journal of Clinical Orthodontics; 13(11); pp. 767-772; Nov. 1979. |
| Ellias et al.; Proteomic analysis of saliva identifies potential biomarkers for orthodontic tooth movement; The Scientific World Journal; vol. 2012; Article ID 647240; dio:10.1100/2012/647240; 7 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2012. |
| Elsasser; Some Observations on the History and Uses of the Kesling Positioner; American Journal of Orthodontics; 36(5); pp. 368-374; May 1, 1950. |
| English translation of Japanese Laid-Open Publication No. 63-11148 to inventor T. Ozukuri (Laid-Open on Jan. 18, 1998) pp. 1-7. |
| Faber et al.; Computerized Interactive Orthodontic Treatment Planning; American Journal of Orthodontics; 73(1); pp. 36-46; Jan. 1978. |
| Felton et al.; A Computerized Analysis of the Shape and Stability of Mandibular Arch Form; American Journal of Orthodontics and Dentofacial Orthopedics; 92(6); pp. 478-483; Dec. 1987. |
| Florez-Moreno; Time-related changes in salivary levels of the osteotropic factors sRANKL and OPG through orthodontic tooth movement; American Journal of Orthodontics and Dentofacial Orthopedics; 143(1); pp. 92-100; Jan. 2013. |
| Friede et al.; Accuracy of Cephalometric Prediction in Orthognathic Surgery; Journal of Oral and Maxillofacial Surgery; 45(9); pp. 754-760; Sep. 1987. |
| Friedrich et al; Measuring system for in vivo recording of force systems in orthodontic treatment-concept and analysis of accuracy; J. Biomech.; 32(1); pp. 81-85; (Abstract Only) Jan. 1999. |
| Futterling et al.; Automated Finite Element Modeling of a Human Mandible with Dental Implants; JS WSCG '98—Conference Program; 8 pages; retrieved from the Internet (https://dspace5.zcu.cz/bitstream/11025/15851/1/Strasser_98.pdf); on Aug. 21, 2018. |
| Gao et al.; 3-D element Generation for Multi-Connected Complex Dental and Mandibular Structure; IEEE Proceedings International Workshop in Medical Imaging and Augmented Reality; pp. 267-271; Jun. 12, 2001. |
| Gim-Alldent Deutschland, “Das DUX System: Die Technik,” 3 pages; (English Translation Included); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date); 2002. |
| Gottleib et al.; JCO Interviews Dr. James A. McNamura, Jr., on the Frankel Appliance: Part 2: Clinical 1-1 Management; Journal of Clinical Orthodontics; 16(6); pp. 390-407; retrieved from the internet (http://www.jco-online.com/archive/print_article.asp?Year=1982&Month=06&ArticleNum+);21 pages; Jun. 1982. |
| Grayson; New Methods for Three Dimensional Analysis of Craniofacial Deformity, Symposium: Computerized Facial Imaging in Oral and Maxillofacial Surgery; American Association of Oral and Maxillofacial Surgeons; 48(8) suppl 1; pp. 5-6; Sep. 13, 1990. |
| Crest, Daniel; Marker-Free Human Motion Capture in Dynamic Cluttered Environments from a Single View-Point, PhD Thesis; 171 pages; Dec. 2007. |
| Guess et al.; Computer Treatment Estimates in Orthodontics and Orthognathic Surgery; Journal of Clinical Orthodontics; 23(4); pp. 262-268; 11 pages; (Author Manuscript); Apr. 1989. |
| Heaven et al.; Computer-Based Image Analysis of Artificial Root Surface Caries; Abstracts of Papers #2094; Journal of Dental Research; 70:528; (Abstract Only); Apr. 17-21, 1991. |
| Highbeam Research; Simulating stress put on jaw. (ANSYS Inc.'s finite element analysis software); 2 pages; retrieved from the Internet (http://static.highbeam.eom/t/toolingampproduction/november011996/simulatingstressputonfa..); on Nov. 5, 2004. |
| Hikage; Integrated Orthodontic Management System for Virtual Three-Dimensional Computer Graphic Simulation and Optical Video Image Database for Diagnosis and Treatment Planning; Journal of Japan KA Orthodontic Society; 46(2); pp. 248-269; 56 pages; (English Translation Included); Feb. 1987. |
| Hoffmann et al.; Role of Cephalometry for Planning of Jaw Orthopedics and Jaw Surgery Procedures; lnformatbnen, pp. 375-396; (English Abstract Included); Mar. 1991. |
| Hojjatie et al.; Three-Dimensional Finite Element Analysis of Glass-Ceramic Dental Crowns; Journal of Biomechanics; 23(11); pp. 1157-1166; Jan. 1990. |
| Huckins; CAD-CAM Generated Mandibular Model Prototype from MRI Data; AAOMS, p. 96; (Abstract Only); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1999. |
| Imani et al.; A wearable chemical-electrophysiological hybrid biosensing system for real-time health and fitness monitoring; Nature Communications; 7; 11650. doi 1038/ncomms11650; 7 pages; May 23, 2016. |
| Invisalign; You were made to move. There's never been a better time to straighten your teeth with the most advanced clear aligner in the world; Product webpage; 2 pages; retrieved from the internet (www.invisalign.com/) on Dec. 28, 2017. |
| Jia et al.; Epidermal biofuel cells: energy harvesting from human perspiration; Angewandle Chemie International Edition; 52(28); pp. 7233-7236; Jul. 8, 2013. |
| Jia et al.; Wearable textile biofuel cells for powering electronics; Journal of Materials Chemistry A; 2(43); pp. 18184-18189; Oct. 14, 2014. |
| JCO Interviews; Craig Andreiko , DDS, MS on the Elan and Orthos Systems; Interview by Dr. Larry W. White; Journal of Clinical Orthodontics; 28(8); pp. 459-468; 14 pages; (Author Manuscript); Aug. 1994. |
| JCO Interviews; Dr. Homer W. Phillips on Computers in Orthodontic Practice, Part 2; Journal of Clinical Orthodontics; 17(12); pp. 819-831; 19 pages; (Author Manuscript); Dec. 1983. |
| Jeerapan et al.; Stretchable biofuel cells as wearable textile-based self-powered sensors; Journal of Materials Chemistry A; 4(47); pp. 18342-18353; Dec. 21, 2016. |
| Jerrold; The Problem, Electronic Data Transmission and the Law; American Journal of Orthodontics and Dentofacial Orthopedics; 113(4); pp. 478-479; 5 pages; (Author Manuscript); Apr. 1998. |
| Jones et al.; An Assessment of the Fit of a Parabolic Curve to Pre- and Post-Treatment Dental Arches; British Journal of Orthodontics; 16(2); pp. 85-93; May 1989. |
| Kamada et.al.; Case Reports on Tooth Positioners Using LTV Vinyl Silicone Rubber; J. Nihon University School of Dentistry; 26(1); pp. 11-29; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1984. |
| Kamada et.al.; Construction of Tooth Positioners with LTV Vinyl Silicone Rubber and Some Case KJ Reports; J. Nihon University School of Dentistry; 24(1); pp. 1-27; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1982. |
| Kanazawa et al.; Three-Dimensional Measurements of the Occlusal Surfaces of Upper Molars in a Dutch Population; Journal of Dental Research; 63(11); pp. 1298-1301; Nov. 1984. |
| Kesling et al.; The Philosophy of the Tooth Positioning Appliance; American Journal of Orthodontics and Oral surgery; 31(6); pp. 297-304; Jun. 1945. |
| Kesling; Coordinating the Predetermined Pattern and Tooth Positioner with Conventional Treatment; American Journal of Orthodontics and Oral Surgery; 32(5); pp. 285-293; May 1946. |
| Kim et al.; Advanced materials for printed wearable electrochemical devices: A review; Advanced Electronic Materials; 3(1); 15 pages; 1600260; Jan. 2017. |
| Kim et al.; Noninvasive alcohol monitoring using a wearable tatto-based iontophoretic-biosensing system; Acs Sensors; 1(8); pp. 1011-1019; Jul. 22, 2016. |
| Kim et al.; Non-invasive mouthguard biosensor for continuous salivary monitoring of metabolites; Analyst; 139(7); pp. 1632-1636; Apr. 7, 2014. |
| Kim et al.; A wearable fingernail chemical sensing platform: pH sensing at your fingertips; Talanta; 150; pp. 622-628; Apr. 2016. |
| Kim et al.; Wearable salivary uric acid mouthguard biosensor with integrated wireless electronics; Biosensors and Bioelectronics; 74; pp. 1061-1068; 19 pages; (Author Manuscript); Dec. 2015. |
| Kleeman et al.; The Speed Positioner; J. Clin. Orthod.; 30(12); pp. 673-680; Dec. 1996. |
| Kochanek; Interpolating Splines with Local Tension, Continuity and Bias Control; Computer Graphics; 18(3); pp. 33-41; Jan. 1, 1984. |
| Kumar et al.; All-printed, stretchable Zn—Ag2o rechargeable battery via, hyperelastic binder for self-powering wearable electronics; Advanced Energy Materials; 7(8); 8 pages; 1602096; Apr. 2017. |
| Kumar et al.; Biomarkers in orthodontic tooth movement; Journal of Pharmacy Bioallied Sciences; 7(Suppl 2); pp. S325-S330; 12 pages; (Author Manuscript); Aug. 2015. |
| Kumar et al.; Rapid maxillary expansion: A unique treatment modality in dentistry; J. Clin. Diagn. Res.; 5(4); pp. 906-911; Aug. 2011. |
| Kunii et al.; Articulation Simulation for an Intelligent Dental Care System; Displays; 15(3); pp. 181-188; Jul. 1994. |
| Kuroda et al.; Three-Dimensional Dental Cast Analyzing System Using Laser Scanning; American Journal of Orthodontics and Dentofacial Orthopedics; 110(4); pp. 365-369; Oct. 1996. |
| Laurendeau et al.; A Computer-Vision Technique for the Acquisition and Processing of 3-D Profiles of 7 Dental Imprints: An Application in Orthodontics; IEEE Transactions on Medical Imaging; 10(3); pp. 453-461; Sep. 1991. |
| Leinfelder et al.; A New Method for Generating Ceramic Restorations: a CAD-CAM System; Journal of the American Dental Association; 118(6); pp. 703-707; Jun. 1989. |
| Manetti et al.; Computer-Aided Cefalometry and New Mechanics in Orthodontics; Fortschr Kieferorthop; 44; pp. 370-376; 8 pages; (English Article Summary Included); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1983. |
| McCann; Inside the ADA; J. Amer. Dent. Assoc, 118:286-294; Mar. 1989. |
| McNamara et al.; Invisible Retainers; J. Clin Orthod.; pp. 570-578; 11 pages; (Author Manuscript); Aug. 1985. |
| McNamara et al.; Orthodontic and Orthopedic Treatment in the Mixed Dentition; Needham Press; pp. 347-353; Jan. 1993. |
| Moermann et al, Computer Machined Adhesive Porcelain Inlays: Margin Adaptation after Fatigue Stress; IADR Abstract 339; J. Dent. Res.; 66(a):763; (Abstract Only); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1987. |
| Moles; Correcting Mild Malalignments—As Easy As One, Two, Three; AOA/Pro Corner; 11(2); 2 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2002. |
| Mormann et al.; Marginale Adaptation von adhasuven Porzellaninlays in vitro; Separatdruck aus:Schweiz. Mschr. Zahnmed.; 95; pp. 1118-1129; 8 pages; (Machine Translated English Abstract); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date); 1985. |
| Nahoum; The Vacuum Formed Dental Contour Appliance; N. Y. State Dent. J.; 30(9); pp. 385-390; Nov. 1964. |
| NASH; CEREC CAD/CAM Inlays: Aesthetics and Durability in a Single Appointment; Dentistry Today; 9(8); pp. 20, 22-23 and 54; Oct. 1990. |
| Nedelcu et al.; “Scanning Accuracy and Precision in 4 Intraoral Scanners: An In Vitro Comparison Based on 3-Dimensional Analysis”; J. Prosthet. Dent.; 112(6); pp. 1461-1471; Dec. 2014. |
| Nishiyama et al.; A New Construction of Tooth Repositioner by LTV Vinyl Silicone Rubber; The Journal of Nihon University School of Dentistry; 19(2); pp. 93-102 (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1977. |
| Ogawa et al.; Mapping, profiling and clustering of pressure pain threshold (PPT) in edentulous oral muscosa; Journal of Dentistry; 32(3); pp. 219-228; Mar. 2004. |
| Ogimoto et al.; Pressure-pain threshold determination in the oral mucosa; Journal of Oral Rehabilitation; 29(7); pp. 620-626; Jul. 2002. |
| Parrilla et al.; A textile-based stretchable multi-ion potentiometric sensor; Advanced Healthcare Materials; 5(9); pp. 996-1001; May 2016. |
| Paul et al.; Digital Documentation of Individual Human Jaw and Tooth Forms for Applications in Orthodontics; Oral Surgery and Forensic Medicine Proc. of the 24th Annual Conf. of the IEEE Industrial Electronics Society (IECON '98); vol. 4; pp. 2415-2418; Sep. 4, 1998. |
| Pinkham; Foolish Concept Propels Technology; Dentist, 3 pages , Jan./Feb. 1989. |
| Pinkham; Inventor's CAD/CAM May Transform Dentistry; Dentist; pp. 1 and 35, Sep. 1990. |
| Ponitz; Invisible retainers; Am. J. Orthod.; 59(3); pp. 266-272; Mar. 1971. |
| Procera Research Projects; Procera Research Projects 1993 ' Abstract Collection; 23 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1993. |
| Proffit et al.; The first stage of comprehensive treatment alignment and leveling; Contemporary Orthodontics, 3rd Ed.; Chapter 16; Mosby Inc.; pp. 534-537; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2000. |
| Proffit et al.; The first stage of comprehensive treatment: alignment and leveling; Contemporary Orthodontics; (Second Ed.); Chapter 15, MosbyYear Book; St. Louis, Missouri; pp. 470-533 Oct. 1993. |
| Raintree Essix & ARS Materials, Inc., Raintree Essix, Technical Magazine Table of contents and Essix Appliances, 7 pages; retrieved from the internet (http://www.essix.com/magazine/defaulthtml) on Aug. 13, 1997. |
| Redmond et al.; Clinical Implications of Digital Orthodontics; American Journal of Orthodontics and Dentofacial Orthopedics; 117(2); pp. 240-242; Feb. 2000. |
| Rekow et al.; CAD/CAM for Dental Restorations—Some of the Curious Challenges; IEEE Transactions on Biomedical Engineering; 38(4); pp. 314-318; Apr. 1991. |
| Rekow et al.; Comparison of Three Data Acquisition Techniques for 3-D Tooth Surface Mapping; Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 13(1); pp. 344-345 (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1991. |
| Rekow; A Review of the Developments in Dental CAD/CAM Systems; Current Opinion in Dentistry; 2; pp. 25-33; Jun. 1992. |
| Rekow; CAD/CAM in Dentistry: A Historical Perspective and View of the Future; Journal Canadian Dental Association; 58(4); pp. 283, 287-288; Apr. 1992. |
| Rekow; Computer-Aided Design and Manufacturing in Dentistry: A Review of the State of the Art; Journal of Prosthetic Dentistry; 58(4); pp. 512-516; Dec. 1987. |
| Rekow; Dental CAD-CAM Systems: What is the State of the Art?; The Journal of the American Dental Association; 122(12); pp. 43-48; Dec. 1991. |
| Rekow; Feasibility of an Automated System for Production of Dental Restorations, Ph.D. Thesis; Univ. of Minnesota, 250 pages, Nov. 1988. |
| Richmond et al.; The Development of the PAR Index (Peer Assessment Rating): Reliability and Validity.; The European Journal of Orthodontics; 14(2); pp. 125-139; Apr. 1992. |
| Richmond et al.; The Development of a 3D Cast Analysis System; British Journal of Orthodontics; 13(1); pp. 53-54; Jan. 1986. |
| Richmond; Recording the Dental Cast in Three Dimensions; American Journal of Orthodontics and Dentofacial Orthopedics; 92(3); pp. 199-206; Sep. 1987. |
| Rudge; Dental Arch Analysis: Arch Form, A Review of the Literature; The European Journal of Orthodontics; 3(4); pp. 279-284; Jan. 1981. |
| Sahm et al.; “Micro-Electronic Monitoring of Functional Appliance Wear”; Eur J Orthod.; 12(3); pp. 297-301; Aug. 1990. |
| Sahm; Presentation of a wear timer for the clarification of scientific questions in orthodontic orthopedics; Fortschritte der Kieferorthopadie; 51 (4); pp. 243-247; (Translation Included) Jul. 1990. |
| Sakuda et al.; Integrated Information-Processing System in Clinical Orthodontics: An Approach with Use of a Computer Network System; American Journal of Orthodontics and Dentofacial Orthopedics; 101(3); pp. 210-220; 20 pages; (Author Manuscript) Mar. 1992. |
| Schafer et al.; “Quantifying patient adherence during active orthodontic treatment with removable appliances using microelectronic wear-time documentation”; Eur J Orthod.; 37(1)pp. 1-8; doi:10.1093/ejo/cju012; Jul. 3, 2014. |
| Schellhas et al.; Three-Dimensional Computed Tomography in Maxillofacial Surgical Planning; Archives of Otolaryngology—Head and Neck Surgery; 114(4); pp. 438-442; Apr. 1988. |
| Schroeder et al; Eds. The Visual Toolkit, Prentice Hall PTR, New Jersey; Chapters 6, 8 & 9, (pp. 153-210,309-354, and 355-428; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1998. |
| Shilliday; Minimizing finishing problems with the mini-positioner; American Journal of Orthodontics; 59(6); pp. 596-599; Jun. 1971. |
| Shimada et al.; Application of optical coherence tomography (OCT) for diagnosis of caries, cracks, and defects of restorations; Current Oral Health Reports; 2(2); pp. 73-80; Jun. 2015. |
| Siemens; CEREC—Computer-Reconstruction, High Tech in der Zahnmedizin; 15 pagesl; (Includes Machine Translation); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date); 2004. |
| Sinclair; The Readers' Corner; Journal of Clinical Orthodontics; 26(6); pp. 369-372; 5 pages; retrived from the internet (http://www.jco-online.com/archive/print_article.asp?Year=1992&Month=06&ArticleNum=); Jun. 1992. |
| Sirona Dental Systems GmbH, CEREC 3D, Manuel utiiisateur, Version 2.0X (in French); 114 pages; (English translation of table of contents included); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date); 2003. |
| Stoll et al.; Computer-aided Technologies in Dentistry; Dtsch Zahna'rztl Z 45, pp. 314-322; (English Abstract Included); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1990. |
| Sturman; Interactive Keyframe Animation of 3-D Articulated Models; Proceedings Graphics Interface '84; vol. 86; pp. 35-40; May-Jun. 1984. |
| The American Heritage, Stedman's Medical Dictionary; Gingiva; 3 pages; retrieved from the interent (http://reference.com/search/search?q=gingiva) on Nov. 5, 2004. |
| The Dental Company Sirona: Cerc omnicam and cerec bluecam brochure: The first choice in every case; 8 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2014. |
| Thera Mon; “Microsensor”; 2 pages; retrieved from the internet (www.english.thera-mon.com/the-product/transponder/index.html); on Sep. 19, 2016. |
| Thorlabs; Pellin broca prisms; 1 page; retrieved from the internet (www.thorlabs.com); Nov. 30, 2012. |
| Tiziani et al.; Confocal principle for macro and microscopic surface and defect analysis; Optical Engineering; 39(1); pp. 32-39; Jan. 1, 2000. |
| Truax; Truax Clasp-Less(TM) Appliance System; The Functional Orthodontist; 9(5); pp. 22-24, 26-28; Sep.-Oct. 1992. |
| Tru-Tatn Orthodontic & Dental Supplies, Product Brochure, Rochester, Minnesota 55902, 16 pages; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 1996. |
| U.S. Department of Commerce, National Technical Information Service, Holodontography: An Introduction to Dental Laser Holography; School of Aerospace Medicine Brooks AFB Tex; Mar. 1973, 40 pages; Mar. 1973. |
| U.S. Department of Commerce, National Technical Information Service; Automated Crown Replication Using Solid Photography SM; Solid Photography Inc., Melville NY,; 20 pages; Oct. 1977. |
| Vadapalli; Minimum intensity projection (MinIP) is a data visualization; 7 pages; retrieved from the internet (https://prezi.com/tdmttnmv2knw/minimum-intensity-projection-minip-is-a-data-visualization/) on Sep. 6, 2018. |
| Van Der Linden et al.; Three-Dimensional Analysis of Dental Casts by Means of the Optocom; Journal of Dental Research; 51(4); p. 1100; Jul.-Aug. 1972. |
| Van Der Linden; A New Method to Determine Tooth Positions and Dental Arch Dimensions; Journal of Dental Research; 51(4); p. 1104; Jul.-Aug. 1972. |
| Van Der Zel; Ceramic-Fused-to-Metal Restorations with a New CAD/CAM System; Quintessence International; 24(A); pp. 769-778; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date); 1993. |
| Van Hilsen et al.; Comparing potential early caries assessment methods for teledentistry; BMC Oral Health; 13(16); doi: 10.1186/1472-6831-13-16; 9 pages; Mar. 2013. |
| Varady et al.; Reverse Engineering of Geometric Models'An Introduction; Computer-Aided Design; 29(4); pp. 255-268; 20 pages; (Author Manuscript); Apr. 1997. |
| Verstreken et al.; An Image-Guided Planning System for Endosseous Oral Implants; IEEE Transactions on Medical Imaging; 17(5); pp. 842-852; Oct. 1998. |
| Warunek et al.; Physical and Mechanical Properties of Elastomers in Orthodonic Positioners; American Journal of Orthodontics and Dentofacial Orthopedics; 95(5); pp. 388-400; 21 pages; (Author Manuscript); May 1989. |
| Warunek et.al.; Clinical Use of Silicone Elastomer Applicances; JCO; 23(10); pp. 694-700; Oct. 1989. |
| Watson et al.; Pressures recorded at te denture base-mucosal surface interface in complete denture wearers; Journal of Oral Rehabilitation 14(6); pp. 575-589; Nov. 1987. |
| Wells; Application of the Positioner Appliance in Orthodontic Treatment; American Journal of Orthodontics; 58(4); pp. 351-366; Oct. 1970. |
| Wikipedia; Palatal expansion; 3 pages; retrieved from the Internet (https://en.wikipedia.org/wiki/Palatal_expansion) on Mar. 5, 2018. |
| Williams; Dentistry and CAD/CAM: Another French Revolution; J. Dent. Practice Admin.; 4(1); pp. 2-5 Jan./Mar. 1987. |
| Williams; The Switzerland and Minnesota Developments in CAD/CAM; Journal of Dental Practice Administration; 4(2); pp. 50-55; Apr./Jun. 1987. |
| Windmiller et al.; Wearable electrochemical sensors and biosensors: a review; Electroanalysis; 25(1); pp. 29-46; Jan. 2013. |
| Wireless Sensor Networks Magazine; Embedded Teeth for Oral Activity Recognition; 2 pages; retrieved on Sep. 19, 2016 from the internet (www.wsnmagazine.com/embedded-teeth/); Jul. 29, 2013. |
| Wishan; New Advances in Personal Computer Applications for Cephalometric Analysis, Growth Prediction, Surgical Treatment Planning and Imaging Processing; Symposium: Computerized Facial Imaging in Oral and Maxilofacial Surgery; p. 5; Presented on Sep. 13, 1990. |
| Witt et al.; The wear-timing measuring device in orthodontics—cui bono? Reflections on the state-of-the-art in wear-timing measurement and compliance research in orthodontics; Fortschr Kieferorthop.; 52(3); pp. 117-125; (Translation Included) Jun. 1991. |
| Wolf; Three-dimensional structure determination of semi-transparent objects from holographic data; Optics Communications; 1(4); pp. 153-156; Sep. 1969. |
| WSCG'98—Conference Program, The Sixth International Conference in Central Europe on Computer Graphics and Visualization '98; pp. 1-7; retrieved from the Internet on Nov. 5, 2004, (http://wscg.zcu.cz/wscg98/wscg98.htm); Feb. 9-13, 1998. |
| Xia et al.; Three-Dimensional Virtual-Reality Surgical Planning and Soft-Tissue Prediction for Orthognathic Surgery; IEEE Transactions on Information Technology in Biomedicine; 5(2); pp. 97-107; Jun. 2001. |
| Yamada et al.; Simulation of fan-beam type optical computed-tomography imaging of strongly scattering and weakly absorbing media; Applied Optics; 32(25); pp. 4808-4814; Sep. 1, 1993. |
| Yamamoto et al.; Optical Measurement of Dental Cast Profile and Application to Analysis of Three-Dimensional Tooth Movement in Orthodontics; Front. Med. Biol. Eng., 1(2); pp. 119-130; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date); 1988. |
| Yamamoto et al.; Three-Dimensional Measurement of Dental Cast Profiles and Its Applications to Orthodontics; Conf. Proc. IEEE Eng. Med. Biol. Soc.; 12(5); pp. 2052-2053; Nov. 1990. |
| Yamany et al.; A System for Human Jaw Modeling Using Intra-Oral Images; Proc. of the 20th Annual Conf. of the IEEE Engineering in Medicine and Biology Society; vol. 2; pp. 563-566; Oct. 1998. |
| Yoshii; Research on a New Orthodontic Appliance: The Dynamic Positioner (D.P.); 111. The General Concept of the D.P. Method and Its Therapeutic Effect, Part 1, Dental and Functional Reversed Occlusion Case Reports; Nippon Dental Review; 457; pp. 146-164; 43 pages; (Author Manuscript); Nov. 1980. |
| Yoshii; Research on a New Orthodontic Appliance: The Dynamic Positioner (D.P.); I. The D.P. Concept and Implementation of Transparent Silicone Resin (Orthocon); Nippon Dental Review; 452; pp. 61-74; 32 pages; (Author Manuscript); Jun. 1980. |
| Yoshii; Research on a New Orthodontic Appliance: The Dynamic Positioner (D.P.); II. The D.P. Manufacturing Procedure and Clinical Applications; Nippon Dental Review; 454; pp. 107-130; 48 pages; (Author Manuscript); Aug. 1980. |
| Yoshii; Research on a New Orthodontic Appliance: The Dynamic Positioner (D.P.); III—The General Concept of the D.P. Method and Its Therapeutic Effect, Part 2. Skeletal Reversed Occlusion Case Reports; Nippon Dental Review; 458; pp. 112-129; 40 pages; (Author Manuscript); Dec. 1980. |
| Zhou et al.; Bio-logic analysis of injury biomarker patterns in human serum samples; Talanta; 83(3); pp. 955-959; Jan. 15, 2011. |
| Zhou et al.; Biofuel cells for self-powered electrochemical biosensing and logic biosensing: A review; Electroanalysis; 24(2); pp. 197-209; Feb. 2012. |
| Grove et al.; U.S. Appl. No. 15/726,243 entitled “Interproximal reduction templates,” filed Oct. 5, 2017. |
| Shanjani et al.; U.S. Appl. No. 16/019,037 entitled “Biosensor performance indicator for intraoral appliances,” filed Jun. 26, 2018. |
| Sato et al.; U.S. Appl. No. 16/041,606 entitled “Palatal contour anchorage,” filed Jul. 20, 2018. |
| Xue et al.; U.S. Appl. No. 16/010,087 entitled “Automatic detection of tooth type and eruption status,” filed Jun. 15, 2018. |
| Sato et al.; U.S. Appl. No. 16/048,054 entitled “Optical coherence tomography for orthodontic aligners,” filed Jul. 27, 2018. |
| Miller et al.; U.S. Appl. No. 16/038,088 entitled “Method and apparatuses for interactive ordering of dental aligners,” filed Jul. 17, 2018. |
| Moalem et al.; U.S. Appl. No. 16/046,897 entitled Tooth shading, transparency and glazing, filed Jul. 26, 2018. |
| Nyukhtikov et al.; U.S. Appl. No. 15/998,883 entitled “Buccal corridor assessment and computation,” filed Aug. 15, 2018. |
| Kopelman et al.; U.S. Appl. No. 16/152,281 entitled “Intraoral appliances for sampling soft-tissue,” filed Oct. 4, 2018. |
| Morton et al.; U.S. Appl. No. 16/177,067 entitled “Dental appliance having selective occlusal loading and controlled intercuspation,” filed Oct. 31, 2018. |
| Elbaz et al.; U.S. Appl. No. 16/198,488 entitled “Intraoral scanner with dental diagnostics capabilities,” filed Nov. 21, 2018. |
| Elbaz et al.; U.S. Appl. No. 16/188,262 entitled “Intraoral scanner with dental diagnostics capabilities,” filed Nov. 12, 2018. |
| beautyworlds.com; Virtual plastic surgery—beautysurge.com announces launch of cosmetic surgery digital imaging services; 5 pages; retrieved from the internet (http://www.beautyworlds.com/cosmossurgdigitalimagning.htm); Mar. 2004. |
| Berland; The use of smile libraries for cosmetic dentistry; Dental Tribunne: Asia pacfic Edition; pp. 16-18; Mar. 29, 2006. |
| Bookstein; Principal warps: Thin-plate splines and decomposition of deformations; IEEE Transactions on pattern analysis and machine intelligence; 11(6); pp. 567-585; Jun. 1989. |
| Cadent Inc.; OrthoCAD ABO user guide; 38 pages; Dec. 21, 2005. |
| Cadent Inc.; Reviewing and modifying an orthoCAD case; 4 pages; Feb. 14, 2005. |
| Daniels et al.; The development of the index of complexity outcome and need (ICON); British Journal of Orthodontics; 27(2); pp. 149-162; Jun. 2000. |
| Dentrix; Dentrix G3, new features; 2 pages; retrieved from the internet (http://www.dentrix.com/g3/new_features/index.asp); on Jun. 6, 2008. |
| Di Giacomo et al.; Clinical application of sterolithographic surgical guides for implant placement: Preliminary results; Journal Periodontolgy; 76(4); pp. 503-507; Apr. 2005. |
| Gansky; Dental data mining: potential pitfalls and practical issues; Advances in Dental Research; 17(1); pp. 109-114; Dec. 2003. |
| Geomagic; Dental reconstruction; 1 page; retrieved from the internet (http://geomagic.com/en/solutions/industry/detal_desc.php) on Jun. 6, 2008. |
| Gottschalk et al.; OBBTree: A hierarchical structure for rapid interference detection; 12 pages; (http://www.cs.unc.edu/?geom/OBB/OBBT.html); retieved from te internet (https://www.cse.iitk.ac.in/users/amit/courses/RMP/presentations/dslamba/presentation/sig96.pdf) on Apr. 25, 2019. |
| gpsdentaire.com; Get a realistic smile simulation in 4 steps with GPS; a smile management software; 10 pages; retrieved from the internet (http://www.gpsdentaire.com/en/preview/) on Jun. 6, 2008. |
| Karaman et al.; A practical method of fabricating a lingual retainer; Am. Journal of Orthodontic and Dentofacial Orthopedics; 124(3); pp. 327-330; Sep. 2003. |
| Mantzikos et al.; Case report: Forced eruption and implant site development; The Angle Orthodontist; 68(2); pp. 179-186; Apr. 1998. |
| Methot; Get the picture with a gps for smile design in 3 steps; Spectrum; 5(4); pp. 100-105; (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2006. |
| OrthoCAD downloads; retrieved Jun. 27, 2012 from the internet (www.orthocad.com/download/downloads.asp); 2 pages; Feb. 14, 2005. |
| Page et al.; Validity and accuracy of a risk calculator in predicting periodontal disease; Journal of the American Dental Association; 133(5); pp. 569-576; May 2002. |
| Patterson Dental; Cosmetic imaging; 2 pages retrieved from the internet (http://patterson.eaglesoft.net/cnt_di_cosimg.html) on Jun. 6, 2008. |
| Rose et al.; The role of orthodontics in implant dentistry; British Dental Journal; 201(12); pp. 753-764; Dec. 23, 2006. |
| Rubin et al.; Stress analysis of the human tooth using a three-dimensional finite element model; Journal of Dental Research; 62(2); pp. 82-86; Feb. 1983. |
| Sarment et al.; Accuracy of implant placement with a sterolithographic surgical guide; journal of Oral and Maxillofacial Implants; 118(4); pp. 571-577; Jul. 2003. |
| Smalley; Implants for tooth movement: Determining implant location and orientation: Journal of Esthetic and Restorative Dentistry; 7(2); pp. 62-72; Mar. 1995. |
| Smart Technology; Smile library II; 1 page; retrieved from the internet (http://smart-technology.net/) on Jun. 6, 2008. |
| Smile-Vision_The smile-vision cosmetic imaging system; 2 pages; retrieved from the internet (http://www.smile-vision.net/cos_imaging.php) on Jun. 6, 2008. |
| Szeliski; Introduction to computer vision: Structure from motion; 64 pages; retrieved from the internet (http://robots.stanford.edu/cs223b05/notes/CS%20223-B%20L10%structurefrommotion1b.ppt, on Feb. 3, 2005. |
| Vevin et al.; Pose estimation of teeth through crown-shape matching; In Medical Imaging: Image Processing of International Society of Optics and Photonics; vol. 4684; pp. 955-965; May 9, 2002. |
| Virtual Orthodontics; Our innovative software; 2 pages; (http://www.virtualorthodontics.com/innovativesoftware.html); retrieved from the internet (https://web.archive.org/web/20070518085145/http://www.virtualorthodontics.com/innovativesoftware.html); (year of pub. sufficiently earlier than effective US filing date and any foreign priority date) 2005. |
| Wiedmann; According to the laws of harmony to find the right tooth shape with assistance of the computer; Digital Dental News; 2nd vol.; pp. 0005-0008; (English Version Included); Apr. 2008. |
| Wong et al.; Computer-aided design/computer-aided manufacturing surgical guidance for placement of dental implants: Case report; Implant Dentistry; 16(2); pp. 123-130; Sep. 2007. |
| Wong et al.; The uses of orthodontic study models in diagnosis and treatment planning; Hong Knog Dental Journal; 3(2); pp. 107-115; Dec. 2006. |
| Yaltara Software; Visual planner; 1 page; retrieved from the internet (http://yaltara.com/vp/) on Jun. 6, 2008. |
| Zhang et al.; Visual speech features extraction for improved speech recognition; 2002 IEEE International conference on Acoustics, Speech and Signal Processing; vol. 2; 4 pages; May 13-17, 2002. |
| Arnone et al.; U.S. Appl. No. 16/235,449 entitled “Method and system for providing indexing and cataloguing of orthodontic related treatment profiles and options,” filed Dec. 28, 2018. |
| Mason et al.; U.S. Appl. No. 16/374,648 entitled “Dental condition evaluation and treatment,” filed Apr. 3, 2019. |
| Brandt et al.; U.S. Appl. No. 16/235,490 entitled “Dental wire attachment,” filed Dec. 28, 2018. |
| Kou; U.S. Appl. No. 16/270,891 entitled “Personal data file,” filed Feb. 8, 2019. |
| Arakawa et al; Mouthguard biosensor with telemetry system for monitoring of saliva glucose: A novel cavitas sensor; Biosensors and Bioelectronics; 84; pp. 106-111; Oct. 2016. |
| O'Leary et al.; U.S. Appl. No. 16/195,701 entitled “Orthodontic retainers,” filed Nov. 19, 2018. |
| Shanjani et al., U.S. Appl. No. 16/206,894 entitled “Sensors for monitoring oral appliances,” filed Nov. 28, 2019. |
| Shanjani et al., U.S. Appl. No. 16/231,906 entitled “Augmented reality enhancements for dental practitioners.” filed Dec. 24, 2018. |
| Kopleman et al., U.S. Appl. No. 16/220,381 entitled “Closed loop adaptive orthodontic treatment methods and apparatuses,” filed Dec. 14, 2018. |
| Number | Date | Country | |
|---|---|---|---|
| 20190175303 A1 | Jun 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62580432 | Nov 2017 | US | |
| 62580427 | Nov 2017 | US | |
| 62692551 | Jun 2018 | US |
