System and method for enameling dental restorations with ceramic inks via inkjet printers

Abstract

Systems and methods of a decision intelligence (DI) based computerized framework that automatically and / or dynamically implements color algorithms for performing staining and enameling of dental restorations are disclosed. The framework operates by utilizing color variations determined through AI / ML based analysis that generates a detailed color map that can be converted into precise printing instructions that control the mixing and layering of dental grade enameling materials. The printing system can employ multiple print heads with different base colors and opacity levels to produce microscopic color gradients and translucency effects that match natural tooth enamel. The disclosed automated process applies a personalized color pattern in microscopic thin layers, ensuring consistent enamel thickness while replicating regional variations on the tooth surface. This approach eliminates manual enameling variability and simultaneously achieves superior customization compared to traditional preset patterns, resulting in dental restorations that seamlessly integrate with existing teeth.

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 627,670, filed January 31, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates generally to the processing of dental prostheses, and more specifically to a computerized framework based on decision intelligence (DI) for automatically and / or dynamically controlling and executing color algorithms for performing staining and glazing of dental prostheses. Summary of the Invention

[0004] As background, glazing of dental restorations is an important finishing step that improves the restoration's resistance to damage and makes it look more realistic. Generally, this glazing is performed manually according to one of several preset coloring patterns.

[0005] This manual enamel application process for dental restorations faces several significant technical and practical limitations that affect production and outcomes. The inherent variability of manual application presents challenges to consistency among technicians, even between restorations performed by the same technician, making standardization difficult and impacting quality control. The process is time-intensive, requiring skilled technicians to carefully apply and blend colors according to a predetermined pattern, thus increasing production costs and turnaround time. The heavy reliance on the expertise and artistic skill of technicians creates bottlenecks and requires lengthy training periods for new technicians; furthermore, manually applied patterns may not adequately represent natural tooth variations.

[0006] Manual application also makes maintaining consistent enamel thickness and coverage challenging, impacting both aesthetic and protective properties. These technical limitations manifest in real-world effects across operational procedures, clinical outcomes, and patient experience. Dental laboratories face higher production costs, longer timelines, and more demanding training requirements, while clinical outcomes can vary significantly across vendors. Patients may experience longer wait times, higher costs, and potential dissatisfaction with aesthetic results or restoration lifespan.

[0007] In reality, the industry as a whole faces numerous obstacles in scaling up operations, increasing reliance on skilled technicians, limited standardization, and higher management costs that impact market competitiveness. These challenges highlight opportunities for technological improvements in the glazing process to enhance consistency, efficiency, and quality.

[0008] To this end, the disclosed systems and methods provide a computerized framework for guiding an inkjet printer, using ceramic inks, to color and glaze ceramic dental restorations, thereby simulating the color and translucency of original or real teeth, for example, as discussed in more detail below.

[0009] According to some implementations, as discussed herein, the disclosed framework is implemented by integrating with and / or co-implementing three-dimensional (3D) printing technology to create personalized and / or dynamically adjusted enamel color matches for each patient. As discussed in more detail below, according to some implementations, the disclosed framework operates by capturing detailed digital images of a patient's existing teeth under controlled lighting conditions and analyzing subtle variations in color, transparency, fluorescence, surface properties, etc., or combinations thereof. Such images are processed by sophisticated computer vision algorithms, for example, that decompose the color composition of the teeth into precise color maps, thereby identifying complex patterns of hues, saturations, and values ​​that constitute the uniqueness of each person's teeth. A proprietary algorithm then translates this detailed color analysis into specific printing instructions, controlling the precise mixing and layering of dental-grade enamel materials to replicate these natural differences. The printing system utilizes multiple printheads with different base colors and opacity levels to achieve a microscopic color gradient and translucency effect that reflects natural tooth enamel.

[0010] According to some implementation schemes, the disclosed framework works by taking into account factors including, but not limited to, material thickness, light diffusion properties, and the interaction between different color layers to ensure that the final printed result accurately matches the patient's natural teeth. By combining artificial intelligence and / or machine learning (AI / ML) capabilities, the framework can continuously improve its color matching accuracy by learning from previous situations and adjusting for variations in printing conditions.

[0011] Therefore, in some implementations, the printing process applies such personalized color patterns in microscopic thin layers, thereby creating an enamel coating with precise control over thickness and coverage. This automated approach eliminates the variability of manual enamel application while achieving a higher level of customization than traditional preset patterns can provide. This framework also interprets regional variations on the tooth surface, replicating the subtle color transitions present in natural teeth, from a slightly darker cervical region to a more translucent incisal edge. Thus, specific implementations of the disclosed systems and methods can yield dental restorations that integrate seamlessly with the patient's existing teeth, providing excellent aesthetic and consistent protective properties.

[0012] According to some embodiments, a method for automatically and / or dynamically controlling and executing a color algorithm for enamelizing a dental prosthesis is disclosed. According to some embodiments, this disclosure provides a non-transitory computer-readable storage medium for performing the aforementioned technical steps of a framework function. The non-transitory computer-readable storage medium has computer-readable instructions tangibly stored thereon or tangibly encoded thereon, which, when executed by a device, cause at least one processor to perform the method for automatically and / or dynamically controlling and executing a color algorithm for enamelizing a dental prosthesis.

[0013] According to one or more embodiments, a system is provided, the system including one or more processors and / or computing devices configured to provide functionality according to such embodiments. According to one or more embodiments, the functionality is embodied in the steps of a method performed by at least one computing device. According to one or more embodiments, program code (or program logic) executed by the processor of the computing device to implement the functionality according to one or more such embodiments is embodied in, embodied by, and / or embodied on a non-transitory computer-readable medium. Attached Figure Description

[0014] The features and advantages of this disclosure will become apparent from the following description of embodiments illustrated in the accompanying drawings, wherein reference numerals denote the same parts in the various views. The drawings are not necessarily drawn to scale, but rather focus on illustrating the principles of this disclosure:

[0015] Figure 1 This is a perspective view of a system for applying ceramic enamel to a dental restoration using an inkjet printhead, according to some embodiments of the present disclosure;

[0016] Figure 2A It is based on some embodiments of this disclosure that are in the first position. Figure 1 A side view of the system;

[0017] Figures 2B and 2C provide some embodiments according to this disclosure. Figure 1 A non-restricted example view of the system;

[0018] Figure 2D Some implementation schemes according to this disclosure are provided. Figure 1 A non-restricted example view of the system;

[0019] Figure 3 It is located in the second position according to some embodiments of this disclosure. Figure 1 A side view of the system;

[0020] Figure 4AIt is based on some embodiments of this disclosure that are in the first orientation. Figure 1 A perspective view of a dental prosthesis and an inkjet printhead;

[0021] Figure 4B This is a second orientation based on some embodiments of this disclosure. Figure 1 Side view of a dental prosthesis and an inkjet printhead;

[0022] Figure 4C It is a third-party approach based on some embodiments of this disclosure. Figure 1 Side view of a dental prosthesis and an inkjet printhead;

[0023] Figure 4D It is based on some embodiments of this disclosure that are in the fourth orientation. Figure 1 Side view of a dental prosthesis and an inkjet printhead;

[0024] Figure 5 Example views of a prosthesis component under a scanner are depicted according to some embodiments of the present disclosure;

[0025] Figure 6 This is a block diagram of exemplary configurations in which the systems and methods disclosed herein can be implemented according to some embodiments of this disclosure;

[0026] Figure 7 This is a block diagram illustrating components of an exemplary system according to some embodiments of the present disclosure; and

[0027] Figure 8 Exemplary workflows according to some embodiments of this disclosure are shown. Detailed Implementation

[0028] This disclosure will now be described more fully below with reference to the accompanying drawings, which form part of this disclosure, and which illustrate certain exemplary embodiments by way of non-limiting illustration. However, the subject matter may be embodied in a variety of different forms, and therefore the subject matter covered or protected by the claims is intended to be construed as not being limited to any of the exemplary embodiments set forth herein; the exemplary embodiments are provided for illustrative purposes only. Likewise, a fairly broad scope is intended for the subject matter protected or covered by the claims. For example, the subject matter may be embodied as a method, apparatus, component, or system, etc. Thus, embodiments may take the form, for example, hardware, software, firmware, or any combination thereof (other than software itself). Therefore, the following detailed description is not intended to be construed as limiting.

[0029] Throughout the specification and claims, terms may have nuanced meanings implied or implied in the context beyond their explicitly stated meanings. Similarly, the phrase "in one embodiment" as used herein does not necessarily refer to the same embodiment, and the phrase "in another embodiment" as used herein does not necessarily refer to different embodiments. For example, the subject matter protected by the claims is intended to include combinations of all or some of the exemplary embodiments.

[0030] Generally speaking, terms can be understood at least in part based on their usage in the context. For example, terms such as “and,” “or,” or “and / or” as used herein can have a variety of meanings that can depend at least in part on the context in which such terms are used. Typically, “or,” when used with a list of related terms such as A, B, or C, is intended to mean A, B, and C, used here in an inclusive sense, and A, B, or C, used here in an exclusive sense. Furthermore, the term “one or more,” as used herein, depends at least in part on the context and can be used to describe any feature, structure, or characteristic in a singular sense, or to describe a combination of features, structures, or characteristics in a plural sense. Similarly, terms such as “a,” “an,” or “the,” depending at least in part on the context, can be understood to convey either a singular or a plural usage. Additionally, the term “based on” can be understood not necessarily to convey a set of exclusive factors, but alternatively allows for the presence of additional factors that are not necessarily explicitly described, which also depends at least in part on the context.

[0031] The present disclosure is described below with reference to block diagrams and operational illustrations of methods and apparatus. It should be understood that each block of a block diagram or operational illustration, and combinations of blocks in a block diagram or operational illustration, may be implemented by analog or digital hardware and computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer (to modify its functionality as detailed herein), a special-purpose computer, an ASIC, or other programmable data processing apparatus, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, implement the function / action specified in one or more blocks of the block diagram or operational illustration. In some alternative embodiments, the functions / actions indicated in the blocks may not occur in the order indicated in the operational illustrations. For example, depending on the functions / actions involved, two blocks shown consecutively may actually be executed substantially concurrently, or these blocks may sometimes be executed in reverse order.

[0032] For the purposes of this disclosure, a non-transitory computer-readable medium (or computer-readable storage medium / medium) stores computer data, which may include computer program code (or computer-executable instructions) in a machine-readable form executable by a computer. By way of example and not limitation, a computer-readable medium may include a computer-readable storage medium for tangible or fixed storage of data, or a communication medium for transient interpretation of a signal containing code. As used herein, a computer-readable storage medium means a physical or tangible storage device (as opposed to a signal), and includes, but is not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for tangible storage of information (such as computer-readable instructions, data structures, program modules or other data). Computer-readable storage media include, but are not limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solid-state memory technologies, optical storage, cloud storage, magnetic storage devices, or any other physical or material medium that can be used to tangibly store desired information or data or instructions and is accessible by a computer or processor.

[0033] For the purposes of this disclosure, the term "server" should be understood as a point of service that provides processing, database, and communication facilities. By way of example and not limitation, the term "server" may refer to a single physical processor having associated communication, data storage, and database facilities, or it may refer to a network or cluster complex of processors and associated network and storage devices, as well as the operating software and one or more database systems and application software that support the services provided by the server. A cloud server is an example.

[0034] For the purposes of this disclosure, "network" should be understood to mean a network of coupled devices that enable exchange of communication, such as between server and client devices or other types of devices, including, for example, between wireless devices coupled via a wireless network. A network may also include mass storage devices, such as network-attached storage (NAS), storage area networks (SANs), content delivery networks (CDNs), or other forms of computer or machine-readable media. A network may include the Internet, one or more local area networks (LANs), one or more wide area networks (WANs), wired connections, wireless connections, cellular connections, or any combination thereof. Similarly, subnetworks that may employ different architectures or be compatible with different protocols may interoperate within a larger network.

[0035] For the purposes of this disclosure, "wireless network" should be understood as coupling client devices to a network. Wireless networks can employ standalone self-organizing networks, mesh networks, wireless LAN (WLAN) networks, cellular networks, etc. Wireless networks can also employ various network access technologies, including Wi-Fi, Long Term Evolution (LTE), WLAN, wireless router mesh, or second-, third-, fourth-, or fifth-generation (2G, 3G, 4G, 5G) cellular technologies, mobile edge computing (MEC), Bluetooth, 802.11b / g / n, etc. Network access technologies can achieve wide-area coverage for devices, such as client devices with varying degrees of mobility.

[0036] In short, wireless networks can actually include any type of wireless communication mechanism through which signals can communicate between devices (such as client devices or computing devices), between networks, or within a network.

[0037] Computing devices may be able to transmit or receive signals, such as via wired or wireless networks, or may be able to process or store signals, such as in memory as a physical memory state, and thus can operate as servers. Therefore, devices capable of operating as servers may include, for example, dedicated rack servers, desktop computers, laptop computers, set-top boxes, integrated devices combining various features (such as two or more features of the aforementioned devices), etc.

[0038] For the purposes of this disclosure, client (or user, entity, subscriber, or customer) devices may include computing devices capable of transmitting or receiving signals, such as via wired or wireless networks. Client devices may include, for example, desktop computers or portable devices such as cellular phones, smartphones, display pagers, radio frequency (RF) devices, infrared (IR) devices, near field communication (NFC) devices, personal digital assistants (PDAs), handheld computers, tablet computers, tablet phones, laptop computers, set-top boxes, wearable computers, smartwatches, integrated or distributed devices combining various features (such as those of the aforementioned devices), etc.

[0039] Client devices may vary in capabilities or features. The subject matter protected by the claims is intended to cover a wide range of potential variations, such as web-enabled client devices or previously mentioned devices may include high-resolution screens (e.g., HD or 4K), one or more physical or virtual keyboards, mass storage devices, one or more accelerometers, one or more gyroscopes, global positioning system (GPS) or other location identification capabilities, or highly functional displays such as touch-sensitive color 2D or 3D displays.

[0040] Some implementation schemes and principles will be discussed in more detail with reference to the accompanying drawings. For background, dental restorations can initially be manufactured (or machined) as a single-color integral part. For example, when a dental restoration is formed by systematically machining away portions of a ceramic block, the resulting restoration can be monochromatic and may have the color of the original ceramic block. Similarly, when dental restorations are additively manufactured by layering and stacking 3D printing materials, the resulting restoration is simply the color of the material used to print the restoration. Given that aesthetics are an important factor in dental restorations, the monochromatic nature of unglazed dental restorations raises problems because they are distinctly different from the surrounding natural teeth, which are typically multi-colored due to years (or decades) of use and staining.

[0041] To address common aesthetic defects in the field (and to enhance the strength of dental restorations), an enamel layer is applied to the restoration after fabrication (or machining) and before insertion into the patient's mouth. Typically, the task of a worker or technician is to apply this enamel layer due to the level of detail required to accurately simulate the color of natural teeth and the mechanical limitations imposed by the time allotted for applying the enamel. However, the limited time and resources available to each worker usually necessitate selecting a coloring pattern from a limited number of options. Therefore, this manual enamel application process is not only time-consuming, but the accuracy of this conventional method is also limited by the skill or ability of the enamel applicator.

[0042] Therefore, an automated system is needed that can determine the appropriate coloring pattern and apply it using an inkjet head that dispenses ceramic ink (e.g., a mixture of ceramic pigments and glazes). By automating the process, the limitations (timeliness and inaccuracy) previously caused by human labor will be reduced, if not completely eliminated. This will not only increase the production capacity of dental restorations, but the system will also improve the aesthetic quality of each restoration produced.

[0043] Although this document references these techniques applied to restorations (e.g., crowns, bridges, implants, etc.), this disclosure should not be construed as limited to restorations, but rather as including any related or known or to be known dental appliances, such as removable appliances (e.g., partial dentures, complete dentures, etc.).

[0044] like Figure 1As shown, a system 10 for applying ceramic enamel to a dental restoration 210 using an inkjet printer is illustrated, comprising an inkjet printhead 100 and a crown retainer 200. The crown retainer 200 is illustrated, comprising the restoration 210, a base 220, and a rotation mechanism 230. In some embodiments (including the illustrated embodiment), the inkjet printhead 100 has a fixed position such that the location of enamel application (e.g., droplet flow 110) from the inkjet printhead 100 has a fixed origin. In these embodiments, different sides of the restoration 210 are exposed to the droplet flow via movement of the restoration 210 itself, the movement of the restoration being possible via the base 220 about the Y-axis (relative to the Y-axis). Figures 2A to 2D and Figure 3 The moving and / or rotating mechanism 230 (view) around the Z-axis (relative to the view) Figures 2A to 2D and Figure 3 This is achieved by moving the view. Figures 4A to 4D Various positions of the restoration 210 relative to the inkjet printhead 100 are illustrated. In other embodiments, the inkjet printhead 100 may be movable, while the restoration 210 is stationary, allowing the inkjet printhead 100 to move around the restoration 210 to apply glaze to each portion of the restoration 210 as needed. In yet another embodiment, both the inkjet printhead 100 and the restoration 210 are movable, allowing each portion of the restoration 210 to be glazed through a combination of movements of the two components.

[0045] According to some implementation schemes, the mobility of the crown retainer 200 is... Figures 2A to 2D and Figure 3 As shown in the image. Figure 2A The crown retainer 200 is shown in a first position, in which the base 220 is positioned at the 6 o'clock position relative to the "clock" defined by the rotation mechanism 230. Figure 3 The crown retainer 200 is shown in a second position, in which the base 220 is positioned at the 3 o'clock position relative to the "clock" defined by the rotating mechanism 230. Figure 2A First position to Figure 3 The change in the second position is achieved via the rotation of the rotation mechanism 230. Because the rotation mechanism 230 is shaped such that the restoration 210 is substantially centered within it, the distance between the inkjet printhead 100 and the restoration 210 remains substantially constant throughout the rotation of the rotation mechanism 230. Similarly, the restoration 210 is positioned relative to the center of the base 220, so rotation of the base 220 does not affect the distance between the restoration 210 and the inkjet printhead 100. Maintaining a consistent distance between the restoration 210 and the inkjet printhead 100 is important for the functionality of the system 10, as inconsistency could lead to inconsistent coloring.

[0046] As shown in Figures 2B and 2C, the following are depicted Figure 2A Additional views of the non-limiting example embodiments depicted in Figure 2B are shown. Figure 2B depicts a view of the printhead 100 and the rotating mechanism 230; and Figure 2C depicts the following as discussed above. Figure 2A The first position view provided in the text.

[0047] Figure 2E provides a functional, non-limiting embodiment of the rotating mechanism 230, as described above. Figure 2A and Figure 3 The points discussed below will be discussed in more detail.

[0048] therefore, Figures 2A to 2D and Figure 3 Such disclosures provide non-limiting printing methods that allow for the dynamic movement of the repair body below the printhead 100 (or more heads).

[0049] Go to Figure 5 This paper depicts a non-limiting example embodiment of a restoration 210 scanned by a scanner 500. Example views 502 and 504 provide example depictions of a scanner used to capture information related to components of a dental restoration. Specifically, according to some embodiments, in order to determine the coloring pattern to be printed, system 10 may utilize the scanner 500 (e.g., an optical scanner) to generate a scan of a patient's actual teeth (or teeth). The scan may be a 3D rendering of the scanned teeth (or teeth) and may be analyzed to derive a coloring pattern for printing. In some embodiments, teeth positioned on either side of the location of the dental restoration (e.g., the space to which the dental restoration will be fixed) may be scanned, such that the coloring pattern is derived based on those teeth closest to the dental restoration to maximize the aesthetic consistency of the dental restoration.

[0050] The exported shading pattern can be a two-dimensional (2D) map or graph that defines multiple points and a color profile (e.g., hue, base color, etc.) corresponding to each point. The 2D map can then be virtually applied to a 3D rendering of the dental restoration (e.g., like a vinyl wrap for a vehicle), where adjustments are made automatically or via human input to determine the shading profile of the entire restoration. In some embodiments, such as those where the dental restoration is a near-perfect replica of another tooth (e.g., a dental restoration for the right incisor based on the left incisor), the shading profile can be derived directly from the other tooth.

[0051] Once the 3D shading profile is established, system 10 can determine the rotational profile of crown retainer 200 and the printing pattern of inkjet printhead 100. The rotational profile may include commands for moving base 220 and rotation mechanism 230, which are coordinated with the printing pattern to ensure that each portion of dental restoration 210 is exposed to ceramic ink. The printing pattern may include commands for dispensing ceramic ink via inkjet printhead 100, specifying the amount, quality (e.g., color combination), and timing of the ink. Specifically, the printing pattern can achieve morphological variations on the crown surface by influencing the velocity and / or volume of specific droplet flows. Both the rotational profile and the printing pattern can be optimized to reduce the total time spent applying enamel.

[0052] This system addresses several significant technical challenges. First, the droplet size of manually applied ceramic ink (approximately 10 micrometers) exceeds the capabilities of most inkjet printheads (<1 micrometer). In some implementations, the system utilizes specially designed inkjet heads capable of allowing larger droplets to pass through. In other implementations, different formulations for ceramic inks have been developed that perform similar functions to the original ceramic ink but with a reduced size, enabling use with standard inkjet heads.

[0053] Second, the standard cyan-yellow-magenta-white (CYMW) color palette used for inkjet printing may not satisfactorily match the diverse hues of multicolored real teeth. In some implementations, the system utilizes a custom set of ink colors, individually or in combination, capable of matching any desired coloring pattern.

[0054] Third, in addition to the coloring pattern, the applied ceramic enamel must also match the translucency (e.g., reflectivity) of the actual tooth. To this end, in addition to the amount of ink applied for a specific coloring pattern, the system can apply various amounts of clear and / or blue (which appear translucent as ceramic) ink. Because these translucent layers differ from the colored layers, the system takes both into account when determining the printed pattern, as an excessively thick enamel layer can affect the fit, appearance, or performance of the dental restoration.

[0055] Go to Figures 6 to 8 The method for achieving such as via Figures 1 to 5 A non-limiting embodiment of the aforementioned functional components of the disclosed system 10 is provided. Therefore, Figure 6 A sample operating environment in which such a system can function is provided; Figure 7 Engine 700 is provided, representing a computerized framework (e.g., color algorithms) for providing novel color matching and glazing techniques as discussed herein; and Figure 8 A process 800 is provided that is described in detail as a non-limiting example implementation for executing engine 700 within a runtime environment.

[0056] refer to Figure 6 The system 600, depicted as similar to System 10 described above, includes User Equipment (UE) 602, Network 604, Cloud System 606, Database 608, and Shading Engine 700. It should be understood that while System 600 is depicted as including such components, it should not be construed as limiting, as those skilled in the art will readily understand that varying numbers of UEs, cloud systems, databases, and networks can be utilized; however, for illustrative purposes, System 600 is about... Figure 6 The examples in the text are used to illustrate the discussion.

[0057] According to some implementation schemes, UE 602 may represent a printer system (e.g., the one discussed above from...). Figures 1 to 5 (System 10 and / or printer). In some implementations, UE 602 may correspond to any other device that can be connected to and control the operation of System 10, such as, for example, mobile phones, tablets, laptops, sensors, Internet of Things (IoT) devices, autonomous machines, and any other device equipped with cellular or wireless or wired transceivers.

[0058] In some implementations, a peripheral device (not shown) may be connected to the UE 602 and can be any type of peripheral device, such as, but not limited to, wearable devices (e.g., smartwatches), printers, displays, speakers, sensors, etc. In some implementations, the peripheral device may be any type of device capable of connecting to the UE 602 via any type of known or known pairing mechanism, including but not limited to Wi-Fi and Bluetooth. ™ Bluetooth Low Energy (BLE), NFC, etc.

[0059] In some implementations, network 604 can be any type of network, such as, but not limited to, wireless networks, cellular networks, the Internet, etc. (as discussed above). Network 604 facilitates the connection of components of system 600, such as... Figure 6 As shown.

[0060] According to some implementations, cloud system 606 can be any type of cloud operating platform and / or network-based system on which applications, operations, and / or other forms of network resources may reside. For example, system 606 can be a content provider, service provider, and / or network provider from which services and / or applications can be accessed, obtained, or executed. For example, system 606 can represent a cloud-based architecture associated with a system provider (e.g., a healthcare company, a private company, etc.) that has associated network resources hosted on the Internet or a private network (e.g., network 604) that implements (via engine 700) the dental management discussed herein.

[0061] In some implementations, cloud system 606 may include servers and / or information databases accessible via network 604. In some implementations, database 608 of cloud system 606 may store datasets of data and metadata associated with local information and / or network information related to the user of UE 602 and UE 602, as well as services and applications provided by cloud system 606 and / or color engine 700.

[0062] In some implementations, for example, cloud system 606 can provide a private / proprietary management platform, thereby enabling and providing services to the engine 700, which corresponds to the novel functional system 606 discussed below, hosting network 604 and other devices / platforms operating on that network.

[0063] In some implementations, the exemplary computer-based systems / platforms, exemplary computer-based devices, and / or exemplary computer-based components of this disclosure may be specifically configured to operate in cloud computing / architecture 606, such as, but not limited to, Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and / or Software as a Service (SaaS) using web browsers, mobile applications, thin clients, terminal emulators, or other endpoints.

[0064] According to some implementations, database 608 may correspond to a data storage device for a platform (e.g., a web hosting platform, such as cloud system 606 as discussed above), multiple platforms, and / or UE 602. Database 608 may receive storage instructions / requests from, for example, engine 700 (and associated microservices), which may be in any known or potentially known format, such as, for example, standard query language (SQL). According to some implementations, database 608 may correspond to any known or potentially known storage device, such as the device's memory or memory stack, a distributed ledger of a distributed network (e.g., a blockchain), a lookup table (LUT), and / or any other type of secure data repository.

[0065] The shader engine 700, as discussed in more detail above and below, may include components for the disclosed functionality. According to some embodiments, the shader engine 700 may be a dedicated machine or processor and may be hosted by a device on network 604, a device within cloud system 606, or a device on UE 602. In some embodiments, the engine 700 may be hosted by a server and / or a set of servers associated with cloud system 606.

[0066] According to some implementation schemes, as discussed in more detail below, the shading engine 700 can be configured to implement and / or control multiple services and / or microservices, each of which is configured to perform multiple workflows associated with the execution of the disclosed dental administration. Non-limiting implementation schemes of such workflows are provided below.

[0067] According to some implementations, as discussed above, the shading engine 700 may run as an application provided by the cloud system 606. In some implementations, the engine 700 may run as an application installed on a server, network location, and / or other type of network resource associated with the system 606. In some implementations, the engine 700 may run as an application installed and / or executed on the UE 602. In some implementations, such an application may be a web-based application accessed by the UE 602 and / or devices from the cloud system 606 via network 604. In some implementations, the engine 700 may be configured and / or installed as an enhanced script, program, or application (e.g., a plugin or extension) of another application or program provided by the cloud system 606 and / or executed on the UE 602.

[0068] like Figure 7 As shown, according to some embodiments, the coloring engine 700 includes an identification module 702, an analysis module 704, a determination module 706, and a control module 708. It should be understood that the engines and modules discussed herein are non-exhaustive, as more or fewer engines and / or modules (or sub-modules) may be applicable to embodiments of the systems and methods discussed. Further details regarding the operation, configuration, and functionality of engine 700 and each of its modules, as well as their role in embodiments of this disclosure, will be discussed below.

[0069] Go to Figure 8 Process 800 provides non-limiting example implementations of the disclosed framework, which provides features, capabilities, and / or functionality for enhancing and / or improving dental restorations.

[0070] According to some implementation schemes, as discussed herein and described in more detail below, process 800 provides disclosed steps for personalized color matching and application for dental restorations through advanced color mapping, simulation, and multilayer printing technologies. The disclosed framework processes input data from multiple sources, including direct digital imaging of the patient's existing teeth and standardized hue references, to generate accurate color and optical property specifications. As discussed herein, this approach fundamentally differs from conventional methods in that it treats color application as a complex, multidimensional optimization problem rather than a simple surface treatment.

[0071] According to some implementations, the framework's architecture includes several integrated computational components. In some implementations, the core color mapping engine analyzes the input source to create a detailed spatial map of optical properties, thereby encoding multiple information channels including basic shading, translucency, and surface characteristics. Such properties can be mapped onto a 3D surface representation using sophisticated texture mapping techniques, where each point can contain multiple layers of optical information. Unlike traditional printing systems, this framework employs a proprietary color space transformation algorithm that takes into account the unique properties of dental materials and their optical interactions.

[0072] According to some implementations, as discussed herein, the key innovations in the configuration and operation of the disclosed framework involve the ability to reverse-render by simulating the interaction between the substrate, the enamel layer, and light to predict the final optical appearance. This simulation considers material thickness, opacity gradients, and the natural optical properties of the teeth to determine precise material deposition parameters. The framework incorporates an AI / ML component that continuously refines color matching accuracy by analyzing the relationship between input specifications and measurements, thereby adapting to variations in substrate material properties and printing conditions.

[0073] According to some implementations, the disclosed color optimization operations employed by this framework can be achieved via sophisticated AI / ML algorithms that translate desired optical properties into specific material combinations and layering sequences. Unlike traditional dental color matching systems that rely on premixed materials with near-normalized hues, the disclosed framework dynamically calculates the precise mixture of matrix materials required to achieve the target optical properties. For example, such operations may include considering phenomena such as conditional color change, where different material combinations can produce visually identical results under specific lighting conditions, resulting in more robust and natural outcomes.

[0074] In some implementations, the framework's material deposition planning component generates detailed instructions for multilayer printing, thereby controlling parameters such as layer thickness, material ratio, and deposition pattern. This enables the production of sophisticated optical effects, such as subsurface scattering and natural color gradients that mimic the internal structure of natural teeth. The framework takes into account material-specific properties, such as opacity, translucency, and light diffusion characteristics, to tailor the deposition strategy to achieve the desired optical results while maintaining structural integrity.

[0075] Furthermore, in some implementations, the framework includes calibration and validation operations that can be used to maintain color accuracy by continuously monitoring and adjusting for changes in matrix material properties, environmental conditions, equipment performance, and other properties and / or characteristics. The framework can employ color measurement and comparison algorithms to validate output against target specifications, automatically initiating adjustment cycles when results fall outside acceptable parameter ranges. This, for example, ensures consistent and predictable results regardless of changes in input materials or conditions.

[0076] In some implementations, the framework incorporates advanced visualization capabilities that allow for real-time previewing and adjustment of the restoration's appearance. This includes simulating different lighting conditions and viewing angles, enabling optimization of both aesthetic and functional properties. The visualization system takes into account complex optical phenomena, such as subsurface scattering and edge effects, providing accurate predictions of the final appearance under a variety of real-world conditions.

[0077] In some implementations, material optimization algorithms can be implemented within this framework to take into account aesthetic and functional requirements, thereby ensuring that color and optical property specifications remain consistent with structural and durability requirements. This includes analyzing material thickness constraints, thermal properties, and wear characteristics, and adjusting color specifications to maintain optimal performance while achieving desired aesthetic results.

[0078] As discussed herein, in some implementations, the framework's adaptability is enhanced by a comprehensive database of material optical properties, color interaction models, and historical performance data. This knowledge base enables the framework to make informed decisions regarding material selection and application strategies, taking into account factors such as material compatibility, environmental stability, and long-term color stability. The database (e.g., database 608) can be continuously updated via AI / ML algorithms that analyze the relationships between specified parameters and measurement results.

[0079] In some implementations, quality control mechanisms can be integrated throughout the framework to monitor each stage of the process, from initial color analysis to final verification. These mechanisms employ statistical process control techniques to identify and correct deviations from target specifications, thereby ensuring consistent and predictable results. The framework maintains detailed records of process parameters and results, enabling continuous improvement of color matching and application strategies.

[0080] In some implementations, advanced error correction algorithms can be used to enable the framework to compensate for various sources of variation and uncertainty. This includes adjustments for differences in material properties between batches, the influence of the environment on the color development process, and differences in substrate characteristics. The framework can automatically modify process parameters to maintain consistent results regardless of these variations, thereby ensuring reliable color matching across different production conditions.

[0081] In some implementations, the framework's output generation operation produces comprehensive specifications for material deposition, including detailed instructions for layer composition, thickness, and sequencing. These specifications take into account both the aesthetic requirements of the restoration and the practical limitations of the manufacturing process, ensuring that the desired optical properties can be reliably and effectively achieved.

[0082] According to some implementation schemes, step 802 of process 800 may be executed by the recognition module 702 of the shading engine 700; step 804 may be executed by the analysis module 704; steps 806 and 808 may be executed by the determination module 706; and steps 810 and 812 may be executed by the control module 708.

[0083] According to some implementations, process 800 begins at step 802, where engine 700 collects data relating to the replacement tooth, the previous tooth, at least one adjacent tooth, or some combination thereof. For example, the data may relate to teeth previously present in the patient, thereby processing the replacement tooth to achieve a match as described herein. In another example, such data may correspond to adjacent teeth, or matching teeth on the opposite side of the mouth (e.g., matching incisors). According to some implementations, such data originates from multiple input modalities, including direct digital imaging, intraoral scans, and normalized hue references. Engine 700 processes this input to extract detailed information about the optical and structural properties of the patient's existing teeth. This data forms the basis for subsequent analysis, ensuring seamless integration of the replacement tooth with the surrounding dentition. The collected data includes key parameters such as base shading, translucency gradient, surface texture, and optical phenomena such as light scattering and reflection.

[0084] As discussed in this paper, the ability to capture detailed and multidimensional data is crucial for personalization. By including information about adjacent teeth, the framework ensures that the appearance of the replacement teeth harmonizes with the patient's natural dentition. Including standardized tone references further enhances the accuracy of the collected data, providing a benchmark for comparison and validation.

[0085] In step 804, engine 700 employs advanced AI / ML algorithms to analyze the collected data. In some implementations, engine 700 processes this input to create detailed spatial maps of optical properties. These maps encode multiple information channels, including but not limited to base shading, translucency gradients, surface texture characteristics, etc. The AI / ML component continuously improves its accuracy by learning from historical data and real-world results.

[0086] In some implementations, the analysis in step 804 can function by treating color matching as a multidimensional optimization operation, considering factors such as, but not limited to, the refractive index, light absorption spectrum, scattering coefficient, etc., or combinations thereof, of dental materials. Unlike traditional methods that rely on visual matching, the implemented AI / ML algorithm identifies complex patterns and relationships within the data. For example, the framework can detect subtle variations in translucency and surface texture that affect the overall appearance of teeth. Furthermore, the integration of AI / ML allows the framework to adapt to variations in input data, such as differences in image quality or material properties. This adaptability ensures that the analysis remains robust and reliable across different clinical scenarios. By fully leveraging the capabilities of AI / ML, the framework achieves a level of accuracy and consistency superior to traditional methods.

[0087] Therefore, in some implementations, this analysis in step 804 may involve engine 700 implementing any type of known or to be known computational analysis techniques, algorithms, mechanisms or processes to analyze the information collected from step 802.

[0088] In some implementations, engine 700 may include a particular trained AI / ML model, a particular machine learning model architecture, a particular machine learning model type (e.g., convolutional neural network (CNN), recurrent neural network (RNN), autoencoder, support vector machine (SVM), etc.), or any other suitable definition of a machine learning model or any suitable combination thereof.

[0089] In some implementations, Engine 700 may be configured to utilize one or more AI / ML techniques, selected from, but not limited to, computer vision, feature vector analysis, decision trees, augmentation, support vector machines, neural networks, nearest neighbor algorithms, Naive Bayes, bagging, random forests, logistic regression, physically based rendering, differentiable rendering, inverse rendering, neural rendering, etc. As a non-limiting example, Engine 700 may implement the XGBoost algorithm for regression and / or classification to analyze dental data and / or patient data, as discussed herein.

[0090] In some implementations, and optionally, in conjunction with any of the implementations described above or below, the neural network technique may be, but is not limited to, a feedforward neural network, a radial basis function network, a recurrent neural network, a convolutional network (e.g., U-net), or other suitable network. In some implementations, and optionally, in conjunction with any of the implementations described above or below, a specific implementation of the neural network may be performed as follows:

[0091] a. Define the neural network architecture / model.

[0092] b. Transfer the input data to the neural network model.

[0093] c. Perform incremental training on the model.

[0094] d. Determine the accuracy for a specific number of time steps.

[0095] e. Apply the trained model to process the newly received input data.

[0096] f. Optionally and in parallel, the trained model can be trained at predetermined periods.

[0097] In some embodiments, and optionally, in conjunction with any of the embodiments described above or below, the trained neural network model may specify the neural network by at least a neural network topology, a series of activation functions, and connection weights. For example, the topology of the neural network may include the configuration of the nodes of the neural network and the connections between such nodes. In some embodiments, and optionally, in conjunction with any of the embodiments described above or below, the trained neural network model may also be specified to include other parameters, including but not limited to bias values / functions and / or aggregation functions. For example, the activation function of a node may be a step function, a sine function, a continuous or piecewise linear function, a sigmoid function, a hyperbolic tangent function, or other type of mathematical function representing a threshold for activating a node. In some embodiments, and optionally, in conjunction with any of the embodiments described above or below, the aggregation function may be a mathematical function that combines (e.g., summation, product, etc.) input signals to a node. In some embodiments, and optionally, in conjunction with any of the embodiments described above or below, the output of the aggregation function may be used as the input to the activation function. In some implementations, and optionally in conjunction with any implementation described above or below, the bias may be a constant value or function that the aggregation function and / or activation function can use to make a node more likely or less likely to be activated.

[0098] In step 806, engine 700 determines a color profile based on the results of data analysis. As mentioned above, the color profile represents a comprehensive specification of the target's optical properties, including but not limited to hue, saturation, brightness, and translucency. The framework's proprietary color space transformation algorithm plays a crucial role in this step, converting the analyzed data into a precise and actionable color profile, as described above.

[0099] In some implementations, the color profile takes into account the 3D properties of the dental restoration and the unique optical properties of the dental materials. By incorporating parameters such as the material's refractive index and light interaction properties, the engine 700 ensures that the color profile accurately represents the desired appearance. In some implementations, the operation performed by the engine 700 in step 806 may take into account and / or explain phenomena such as conditional discoloration, thereby ensuring that the restoration appears natural under various lighting conditions.

[0100] In some implementations, the color profile determination in step 806 balances aesthetic and functional requirements. For example, engine 700 can adjust the color profile to take into account structural considerations, such as the thickness of the restoration or its compatibility with the underlying material. This functional approach ensures that the final restoration not only looks natural (e.g., according to a set of color thresholds) but also performs reliably in the oral environment.

[0101] In step 808, engine 700 determines a rotational profile based on the data analysis performed in step 804. As described above, the rotational profile specifies the optimal orientation of the restoration within the patient's oral cavity, thereby ensuring that its optical and structural properties are aligned with the surrounding dentition. In some embodiments, step 808 may involve a texture mapping operation that projects the analyzed optical properties onto a 3D model of the restoration.

[0102] As described above, the rotational profile plays a crucial role in achieving seamless integration between the replacement tooth and the natural dentition. By taking into account factors such as the alignment of translucency gradients and surface texture patterns, the operation of Engine 700 ensures that the restoration appears indistinguishable from adjacent teeth. In some embodiments, step 808 may also involve further analysis via AI / ML to optimize the orientation of the restoration to maximize its structural stability and functional performance.

[0103] In step 810, engine 700 compiles a shading map integrating the color profile and the rotation profile. In some embodiments, the generated map may include detailed specifications for the application of stains and enamels, translucency gradients, and / or other optical effects. For example, analyzing the shading map may also include information derived, determined, or otherwise identified from analysis of stain and enamel information (e.g., AI / ML analysis) that identifies the translucency of the replacement tooth. Thus, specifications for such maps can be defined such that desired optical properties are achieved through precise layering and material combinations of color (and / or stains and / or enamels), which, as described above, can be compiled and / or determined via AI / ML analysis of the profiles from steps 806 and 808.

[0104] According to some implementations, the color mapping process in step 810 involves the generation of detailed instructions for multi-layer printing. These instructions specify parameters such as layer thickness, material ratio, deposition pattern, etc. By taking into account material-specific properties, such as viscosity and curing behavior, engine 700 ensures that the color mapping is both accurate and functional. Therefore, the color mapping provides a comprehensive blueprint for the production process, enabling components of system 10 to be used to deliver the desired output on replacement teeth.

[0105] In some implementations, the mapping compilation / generation in step 810 may further involve executing a predictive model that simulates a renderable data structure (or file) digitally representing the final appearance of the restoration under real-world conditions (regarding the patient's current oral environment). Indeed, while those skilled in the art will recognize that the mappings discussed herein can be 3D mappings, as discussed above, this should not be construed as limiting, as such mappings can be n-based mappings that may include, for example, time-varying 2D mappings (as discussed above) or four-dimensional (4D) mappings, without departing from the scope of this disclosure.

[0106] In step 812, engine 700 performs printer operations based on computer-executable instructions derived from the color map. For example, as described above regarding... Figures 1 to 5 The instructions discussed can be provided to system 10 in accordance with the color mapping in step 810. Therefore, the disclosed material deposition plan translates the color mapping into specific commands for the printing apparatus. These commands control parameters such as layer composition, thickness, and ordering, thereby ensuring faithful reproduction of the desired optical properties. Thus, the execution of the printer operation results in a restoration that perfectly mimics the natural appearance of teeth.

[0107] According to some implementation schemes, as a non-limiting example, based on at least the above regarding Figure 8 The discussion outlines a framework that begins with a comprehensive visual and instrumental analysis of the patient's dentition (e.g., teeth, adjacent / near teeth, oral cavity, etc.) to identify key visual properties necessary for accurate replication. This process can incorporate advanced imaging techniques such as digital photography, intraoral scanning, and spectrophotometry to capture high-resolution details of the tooth structure. The framework utilizes digital tone matching tools to analyze color distribution, translucency, and surface texture, ensuring a comprehensive assessment of optical properties.

[0108] Once the initial visual properties are identified, the framework can apply AI / ML modeling software to generate a three-dimensional representation of the patient's teeth, as discussed above. This software can integrate data from imaging tools to build accurate models highlighting variations in hue, chroma, and values. In some implementations, AI / ML algorithms can help identify subtle patterns in enamel and dentin, enabling precise differentiation of tonal areas and surface irregularities. The framework may also include analysis of fluorescence and opalescence properties to ensure the synthetic replica achieves a natural aesthetic appearance.

[0109] Following such digital analysis, the framework can employ a systematic approach to select staining agents and enamels and apply them to restorative materials. In some implementations, the system can provide recommendations for specific staining techniques based on the acquired data, thereby optimizing the layering approach to achieve the desired color depth and light reflection. The framework can incorporate a controlled application process, where staining agents are applied in incremental layers to replicate intrinsic and extrinsic staining. For example, the first stage may include applying intrinsic staining agents to mimic the deep staining present within natural dentin, while subsequent layers may focus on enhancing enamel translucency and surface gloss, etc.

[0110] In some implementations, the framework can integrate automated air brush and / or digital deposition technologies to ensure consistent dye application. Such automated methods allow for precise control of dye concentration, gradient transitions, and drying times, resulting in enhanced customization and reproducibility. The framework can also employ a curing scheme that utilizes light or heat to stabilize dye application before the glazing stage.

[0111] In some implementations, enamel application within the framework can serve a dual purpose: i) enhancing final aesthetics and ii) ensuring long-term durability. In some implementations, the enamel can be customized to mimic the reflective properties of natural tooth enamel. The framework can utilize a controlled firing process to achieve appropriate gloss, adjusting temperature and duration based on the restoration's material composition, as described above. Additionally, the framework may include an iterative validation process where the enamel restoration is compared to the patient's natural dentition under various lighting conditions to confirm accuracy.

[0112] In some implementations, the framework may integrate a polishing stage for finely adjusting surface texture to further refine the appearance of the restoration. In some implementations, precision abrasives and diamond paste may be used to smooth microscopic irregularities while maintaining the integrity of the applied enamel. The framework may also include final spectrophotometric analysis to verify that the restoration meets predefined aesthetic parameters.

[0113] Therefore, in some implementations, this framework provides a structured, technology-driven approach to analyze, stain, and enamel dental restorations, ensuring that the final result accurately replicates the patient's natural dentition. By fully leveraging advanced imaging, digital modeling, automated application techniques, and systematic validation, this framework enables highly accurate and aesthetically pleasing results in the synthesis of dental restorations.

[0114] Therefore, as discussed in this paper, the integration of novel computational operations into the workflow of Engine 700 represents a significant advancement in dental prosthetics. Every step of Process 800, from data collection to the execution of printer operations, fully leverages advanced computational techniques to achieve highly personalized and accurate results. By treating color matching as a multidimensional optimization problem, this framework overcomes the limitations of traditional methods and sets a new standard for accuracy and efficiency in the field.

[0115] As used herein, the terms “computer engine” and “engine” refer to at least one software component and / or a combination of at least one software component and at least one hardware component that is designed / programmed / configured to manage / control other software components and / or hardware components (such as libraries, software development kits (SDKs), objects, etc.).

[0116] Examples of hardware components may include processors, microprocessors, circuits, circuit elements (e.g., transistors, resistors, capacitors, inductors, etc.), integrated circuits, application-specific integrated circuits (ASICs), programmable logic devices (PLDs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs), logic gates, registers, semiconductor devices, chips, microchips, chipsets, etc. In some implementations, the one or more processors may be implemented as Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC) processors; x86 instruction set compatible processors, multi-core or any other microprocessor or central processing unit (CPU). In various specific implementations, the one or more processors may be dual-core processors, dual-core mobile processors, etc.

[0117] As used herein, computer-related systems, computer systems, and systems include any combination of hardware and software. Examples of software may include software components, programs, applications, operating system software, middleware, firmware, software modules, routines, subroutines, functions, methods, programs, software interfaces, application interfaces (APIs), instruction sets, computer code, computer code segments, words, values, symbols, or any combination thereof. The determination of whether to use hardware and / or software components to implement an implementation scheme can vary based on any number of factors, such as desired computing speed, power levels, thermal tolerance, processing cycle budget, input data rate, output data rate, memory resources, data bus speed, and other design or performance limitations.

[0118] For the purposes of this disclosure, a module is a software, hardware, or firmware (or a combination thereof) system, process, or functionality or a component thereof that performs or facilitates the processes, features, and / or functions (with or without human interaction or enhancements) described herein. A module may include submodules. The software components of a module may be stored on a computer-readable medium for execution by a processor. A module may be integrated into one or more servers, or loaded and executed by one or more servers. One or more modules may be grouped into engines or applications.

[0119] One or more aspects of at least one embodiment can be implemented by representative instructions stored on a machine-readable medium representing various logic within a processor, which, when read by a machine, causes the machine to generate logic to perform the techniques described herein. Such a representation, referred to as an "IP core," can be stored on a tangible machine-readable medium and supplied to various customers or manufacturing facilities for loading into manufacturing machines that generate logic or processors. It should be noted that the various embodiments described herein can, of course, be implemented using any suitable hardware and / or computational software language (e.g., C++, Objective-C, Swift, Java, JavaScript, Python, Perl, QT, etc.).

[0120] For example, exemplary software specifically programmed according to one or more principles of this disclosure may be downloaded from a network (e.g., a website) as a standalone product or as an add-on package for installation in existing software applications. For example, exemplary software specifically programmed according to one or more principles of this disclosure may also be used as a client-server software application or as a web-enabled software application. For example, exemplary software specifically programmed according to one or more principles of this disclosure may also be embodied as a software package installed on a hardware device.

[0121] For the purposes of this disclosure, the terms “user,” “subscriber,” “consumer,” or “customer” should be understood to refer to users of one or more applications as described herein and / or consumers of data provided by a data provider. By way of example and not limitation, the terms “user” or “subscriber” may refer to a person who receives data provided by a data or service provider via the Internet in a browser session, or may refer to an automated software application that receives, stores, or processes data. Those skilled in the art will recognize that the methods and systems of this disclosure can be implemented in many ways and are therefore not limited to the foregoing exemplary embodiments and examples. In other words, functional elements performed by one or more components in various combinations of hardware and software or firmware, and the various functions, may be distributed at the client level or the server level or both in a software application. In this regard, any number of features of the different embodiments described herein may be combined into one or more embodiments, and alternative embodiments having fewer or more features than all those described herein are possible.

[0122] Functionality can also be distributed, wholly or partially, among multiple components in ways now known or to become known. Therefore, numerous software / hardware / firmware combinations are possible in implementing the functions, features, interfaces, and preferences described herein. Furthermore, the scope of this disclosure covers conventionally known methods for performing the described features and functions and interfaces, as well as those changes and modifications that may be made to the hardware or software or firmware components described herein, as will be understood by those skilled in the art now and thereafter.

[0123] Furthermore, the embodiments of the methods presented and described as flowcharts in this disclosure are provided by way of example to provide a more complete understanding of the technology. The disclosed methods are not limited to the operational and logical flows presented herein. Alternative embodiments are envisioned in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are executed independently.

[0124] While various embodiments have been described for the purposes of this disclosure, such embodiments should not be construed as limiting the teachings of this disclosure to those embodiments. Various changes and modifications may be made to the above-described elements and operations to obtain results that remain within the scope of the systems and processes described in this disclosure.

Claims

1. A method, the method comprising: The application identifies data related to a set of teeth associated with a patient, the set of teeth including at least one existing tooth and a replacement tooth; The application analyzes the data; The color profile is determined by the application based on the analysis; The application determines the rotation profile based on the analysis. The application compiles a shading map based on the color profile and the rotation profile, the shading map including specifications for layering for material composition; as well as The application performs printer operations based on the color mapping, the printer operations including printing on the replacement tooth according to the color mapping.

2. The method of claim 1, wherein the color profile corresponds to the replacement tooth, the color profile including information derived from the color map of the at least one existing tooth.

3. The method of claim 1, wherein the rotational profile corresponds to the replacement tooth, and the rotational profile includes the orientation of the patient's oral cavity.

4. The method of claim 1, further comprising the application analyzing the color profile and the rotation profile to determine the shading map.

5. The method of claim 1, wherein the color mapping is a data structure including information relating to at least one of the dyes or glazes.

6. The method of claim 5, further comprising analyzing the data structure and determining the translucency of the replacement tooth based on the information relating to at least one of the staining agent or enamel, wherein the printer operation is further based on the determined translucency.

7. The method of claim 5, further comprising generating a simulation based on the data structure, the simulation providing a visible display of the final appearance of the prosthesis of the replacement tooth within the current condition of the patient's oral cavity.

8. The method of claim 1, wherein the color mapping comprises parameters selected from the group consisting of: layer thickness, composition, material ratio, ordering, and deposition mode.

9. The method of claim 1, wherein the application includes an artificial intelligence (AI) model.

10. A system comprising: Processor, the processor being configured to: The application identifies data related to a set of teeth associated with a patient, the set of teeth including at least one existing tooth and a replacement tooth; The application analyzes the data; The color profile is determined by the application based on the analysis; The application determines the rotation profile based on the analysis. The application compiles a shading map based on the color profile and the rotation profile, the shading map including specifications for layering for material composition; as well as The application performs printer operations based on the color mapping, the printer operations including printing on the replacement tooth according to the color mapping.

11. The system of claim 10, wherein the color profile corresponds to the replacement tooth, the color profile including information derived from the color map of the at least one existing tooth.

12. The system of claim 10, wherein the rotational profile corresponds to the replacement tooth, and the rotational profile includes the orientation of the patient's oral cavity.

13. The system of claim 10, wherein the processor is further configured to cause the application to analyze the color profile and the rotation profile to determine the shading map.

14. The system of claim 10, wherein the processor is further configured to analyze the color map and determine the translucency of the replacement tooth based on information relating to at least one of the stains or enamels within the color map, wherein the printer operation is further based on the determined translucency.

15. The system of claim 10, wherein the color mapping comprises parameters selected from the group consisting of: layer thickness, composition, material ratio, ordering, and deposition pattern.

16. A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions, which, when executed by a device, perform methods comprising: The application identifies data related to a set of teeth associated with a patient, the set of teeth including at least one existing tooth and a replacement tooth; The application analyzes the data; The color profile is determined by the application based on the analysis; The application determines the rotation profile based on the analysis. The application compiles a shading map based on the color profile and the rotation profile, the shading map including specifications for layering for material composition; as well as The application performs printer operations based on the color mapping, the printer operations including printing on the replacement tooth according to the color mapping.

17. The non-transitory computer-readable storage medium of claim 16, wherein the color profile corresponds to the replacement tooth, the color profile including information derived from a color map of the at least one existing tooth.

18. The non-transitory computer-readable storage medium of claim 16, wherein the rotational profile corresponds to the replacement tooth, the rotational profile including the orientation of the patient's oral cavity.

19. The non-transitory computer-readable storage medium of claim 16, further comprising the application analyzing the color profile and the rotation profile to determine the shading map.

20. The non-transitory computer-readable storage medium of claim 16, further comprising analyzing a tinting profile and determining the translucency of the replacement tooth based on information relating to at least one of a stain or enamel within the tinting map, wherein the printer operation is further based on the determined translucency.

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