Patent Application
20250239342
The present disclosure generally relates to generating treatment plans and more particularly to generating restorations based on analyzing tooth defects, calculating possible treatment plans or restoration options based on triangulation of the tooth defects and enforcement of material constraints, and selecting an optimal treatment plan or restoration option from the possible treatment plans or restoration options.
Traditional dental treatment planning may rely on assessment by dental professionals, often involving visual inspection, and subjective judgment. However, these methods may lack objectivity, efficiency, and precision as some dental practitioners and patients may not have all the available data to compare and contrast patient specific restorations options.
In one aspect, a method includes examining a dental defect in an oral cavity, performing a minimally invasive procedure to detect or remove at least a portion of the dental defect, scanning, responsive to the performing, the oral cavity including a residual tooth substance remaining after the performing, to obtain an intraoral scan, computing one or more constraints for one or more restoration materials, generating a plurality of treatment plans for the one or more restoration materials based on triangulation of the dental defect and enforcement of the one or more constraints of the one or more restoration materials, each treatment plan including a geometry of a restoration option and selecting from the plurality of treatment plans an optimal restoration option. The method may also include, for the minimally invasive procedure, an excavation of tooth material or a detection of dental caries.
In one aspect, a system is disclosed that includes a processor. The system also includes a memory storing instructions that, when executed by the processor, configure the apparatus to examine a dental defect in an oral cavity, perform a minimally invasive procedure to detect or remove at least a portion of the dental defect, scan, responsive to the performing, the oral cavity including a residual tooth substance remaining after the performing, to obtain an intraoral scan, compute one or more constraints for one or more restoration materials, generate a plurality of treatment plans for the one or more restoration materials based on triangulation of the dental defect and enforcement of the one or more constraints of the one or more restoration materials, each treatment plan including a geometry of a restoration option, and select from the plurality of treatment plans an optimal restoration option.
In yet another aspect, a non-transitory computer-readable storage medium is including instructions that when executed by a computer, cause the computer to perform the methods described herein. Even further, any of the methods described herein may be performed automatically with the aid of one or more processors.
Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.
To easily identify the discussion of any particular element or act, the most significant digit or digits in a reference number refer to the figure number in which that element is first introduced.
The illustrative embodiments recognize that dental treatment planning may depend on limitations such as variability in practitioner expertise, subjectivity in diagnosis, and time-consuming processes. Furthermore, integration of diverse diagnostic data sources, patient records, and treatment options may be challenging without a judicious approach that takes into consideration all possible options for treatment, constraints related to implementing the options, patient specific data and input preferences.
The illustrative embodiments disclose examining a dental defect in an oral cavity, performing a minimally invasive procedure to detect or remove at least a portion of the dental defect, and scanning, responsive to the performing, the oral cavity including a residual tooth substance remaining after the performing, to obtain an intraoral scan. The illustrative embodiments compute one or more parameters of the dental defect or residual tooth substance, generating a plurality of treatment plans for one or more restoration materials based on triangulation of the dental defect and enforcement of constraints of the one or more restoration materials, each treatment plan including a geometry of a restoration option and selecting from the plurality of treatment plans an optimal restoration option.
The illustrative embodiments are described with respect to certain types of machines. The illustrative embodiments are also described with respect to other scenes, subjects, measurements, devices, data processing systems, environments, components, and applications only as examples. Any specific manifestations of these and other similar artifacts are not intended to be limiting to the disclosure. Any suitable manifestation of these and other similar artifacts can be selected within the scope of the illustrative embodiments.
Furthermore, the illustrative embodiments may be implemented with respect to any type of data, data source, or access to a data source over a data network. Any type of data storage device may provide the data to an embodiment of the disclosure, either locally at a data processing system or over a data network, within the scope of the disclosure. Where an embodiment is described using a mobile device, any type of data storage device suitable for use with the mobile device may provide the data to such embodiment, either locally at the mobile device or over a data network, within the scope of the illustrative embodiments.
The illustrative embodiments are described using specific code, hardware, algorithms, designs, architectures, protocols, layouts, schematics, and tools only as examples and are not limiting. Furthermore, the illustrative embodiments are described in some instances using particular software, tools, and data processing environments only as an example for the clarity of the description. The illustrative embodiments may be used in conjunction with other comparable or similarly purposed structures, systems, applications, or architectures. For example, other comparable devices, structures, systems, applications, or architectures therefor, may be used in conjunction with such embodiment of the disclosure within the scope of the disclosure. An illustrative embodiment may be implemented in hardware, software, or a combination thereof.
The examples in this disclosure are used only for the clarity of the description and are not limiting to the illustrative embodiments. Additional data, operations, actions, tasks, activities, and manipulations will be conceivable from this disclosure and the same are contemplated within the scope of the illustrative embodiments.
Any advantages listed herein are only examples and are not intended to be limiting to the illustrative embodiments. Additional or different advantages may be realized by specific illustrative embodiments. Furthermore, a particular illustrative embodiment may have some, all, or none of the advantages listed above.
Clients or servers are only example roles of certain data processing systems connected to network/communication infrastructure 102 and are not intended to exclude other configurations or roles for these data processing systems. Server 104 and server 106 couple to network/communication infrastructure 102 along with storage unit 108. Software applications may execute on any computer in data processing environment 100. Client 110, client 112, client 114 are also coupled to network/communication infrastructure 102. Client 110 may be a dental acquisition unit with a display. A data processing system, such as server 104 or server 106, or clients (client 110, client 112, client 114) may include data and may have software applications or software tools executing thereon.
Only as an example, and without implying any limitation to such architecture,
Intra-oral camera 122 includes one or more sensors, such as separate sensors, which capture surfaces of tooth and defects in tooth.
Client application 120 or server application 116 implement an embodiment described herein. Client application 120 and/or server application 116 can use data from intra-oral camera 122 for tooth defect triangulation and generate restoration options. Client application 120 can also execute in any of data processing systems (server 104 or server 106, client 110, client 112, client 114), such as client server application 116 in server 104 and need not execute in the same system as client 110.
Machine learning engine 126 may compute an optimal restoration option for tooth defects by classifying characteristics of a generated plurality of possible treatment plans. Machine learning engine 126 may be a part of, or separate from server 104 or clients 110, 112 and 114. The machine learning engine 126 may be trained based on characteristics of a plurality of test treatment plans.
Server 104, server 106, storage unit 108, client 110, client 112, client 114, may couple to network/communication infrastructure 102 using wired connections, wireless communication protocols, or other suitable data connectivity. Client 110, client 112 and client 114 may be, for example, personal computers or network computers.
In the depicted example, server 104 may provide data, such as boot files, operating system images, and applications to client 110, client 112, and client 114. Client 110, client 112 and client 114 may be clients to server 104 in this example. Client 110, client 112 and client 114 or some combination thereof, may include their own data, boot files, operating system images, and applications. Data processing environment 100 may include additional servers, clients, and other devices that are not shown. Server 104 includes the server application 116 that may be configured to implement one or more of the functions described herein for displaying restoration proposals in accordance with one or more embodiments.
Server 106 may include a search engine configured to search stored files such as images of patient teeth for comparison in response to a request for detecting tooth defects. In the depicted example, data processing environment 100 may be the Internet. Network/communication infrastructure 102 may represent a collection of networks and gateways that use the Transmission Control Protocol/Internet Protocol (TCP/IP) and other protocols to communicate with one another. At the heart of the Internet is a backbone of data communication links between major nodes or host computers, including thousands of dental practices, commercial, governmental, educational, and other computer systems that route data and messages. Of course, data processing environment 100 also may be implemented as a number of different types of networks, such as for example, an intranet, a local area network (LAN), or a wide area network (WAN).
Among other uses, data processing environment 100 may be used for implementing a client-server environment in which the illustrative embodiments may be implemented. A client-server environment enables software applications and data to be distributed across a network such that an application functions by using the interactivity between a client data processing system and a server data processing system. Data processing environment 100 may also employ a service-oriented architecture where interoperable software components distributed across a network may be packaged together as coherent business applications. Data processing environment 100 may also take the form of a cloud, and employ a cloud computing model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service.
With reference to
Data processing system 200 is described as a computer only as an example, without being limited thereto. Implementations in the form of other devices, may modify data processing system 200, such as by adding a touch interface, and even eliminate certain depicted components from data processing system 200 without departing from the general description of the operations and functions of data processing system 200 described herein.
In the depicted example, data processing system 200 employs a hub architecture including NorthBridge and memory controller hub (NB/MCH) 202 and SouthBridge and input/output (I/O) controller hub (SB/ICH) 204. Processing unit 206, main memory 208, and graphics processor 210 are coupled to North Bridge and memory controller hub (NB/MCH) 202. Processing unit 206 may include one or more processors and may be implemented using one or more heterogeneous processor systems. Processing unit 206 may be a multi-core processor. Graphics processor 210 may be coupled to North Bridge and memory controller hub (NB/MCH) 202 through an accelerated graphics port (AGP) in certain implementations.
In the depicted example, local area network (LAN) adapter 212 is coupled to South Bridge and input/output (I/O) controller hub (SB/ICH) 204. Audio adapter 216, keyboard and mouse adapter 220, modem 222, read only memory (ROM) 224, universal serial bus (USB) and other ports 232, and PCI/PCIe devices 234 are coupled to South Bridge and input/output (I/O) controller hub (SB/ICH) 204 through bus 218. Hard disk drive (HDD) or solid-state drive (SSD) 226a and CD-ROM 230 are coupled to South Bridge and input/output (I/O) controller hub (SB/ICH) 204 through bus 228. PCI/PCIe devices 234 may include, for example, Ethernet adapters, add-in cards, and PC cards for notebook computers. PCI uses a card bus controller, while PCIe does not. Read only memory (ROM) 224 may be, for example, a flash binary input/output system (BIOS). Hard disk drive (HDD) or solid-state drive (SSD) 226a and CD-ROM 230 may use, for example, an integrated drive electronics (IDE), serial advanced technology attachment (SATA) interface, or variants such as external-SATA (eSATA) and micro-SATA (mSATA). A super I/O (SIO) device 236 may be coupled to South Bridge and input/output (I/O) controller hub (SB/ICH) 204 through bus 218.
Memories, such as main memory 208, read only memory (ROM) 224, or flash memory (not shown), are some examples of computer usable storage devices. Hard disk drive (HDD) or solid-state drive (SSD) 226a, CD-ROM 230, and other similarly usable devices are some examples of computer usable storage devices including a computer usable storage medium.
An operating system runs on processing unit 206. The operating system coordinates and provides control of various components within data processing system 200 in
Instructions for the operating system, the object-oriented programming system, and applications or programs, such as server application 116 and client application 120 in
Furthermore, in one case, code 226b may be downloaded over network 214a (such as network/communication infrastructure 102) from remote system 214b, where similar code 214c is stored on a storage device 214d in another case, code 226b may be downloaded over network 214a to remote system 214b, where downloaded code 214c is stored on a storage device 214d.
A communications unit may include one or more devices used to transmit and receive data, such as a modem or a network adapter. A memory may be, for example, main memory 208 or a cache, such as the cache found in North Bridge and memory controller hub (NB/MCH) 202. A processing unit may include one or more processors or CPUs.
Where a computer or data processing system is described as a virtual machine, a virtual device, or a virtual component, the virtual machine, virtual device, or the virtual component operates in the manner of data processing system 200 using virtualized manifestation of some or all components depicted in data processing system 200. For example, in a virtual machine, virtual device, or virtual component, processing unit 206 is manifested as a virtualized instance of all or some number of hardware processing units 206 available in a host data processing system, main memory 208 is manifested as a virtualized instance of all or some portion of main memory 208 that may be available in the host data processing system, and Hard disk drive (HDD) or solid-state drive (SSD) 226a is manifested as a virtualized instance of all or some portion of Hard disk drive (HDD) or solid-state drive (SSD) 226a that may be available in the host data processing system. The host data processing system in such cases is represented by data processing system 200.
Turning now to
Treatment plans may be generated by computing one or more constraints for one or more restoration materials that can be used to produce restorations 308 to correct the defects. Constraints may also include constraints of the individual patient such as tooth/defect position. The treatment plans may further be generated by performing a triangulation of the dental defects (such as a volume occupied by the dental defect 518, see
Turning back to
As shown in block 408 of
The computed parameters may then be used for the visualization and/or as part of one or more inputs to a proposal module used in selecting an optimal restoration option from the possible restoration options visualized, as shown in block 410. The proposal module may be based on, for example, statistical computations, artificial intelligence, model simulations and/or patient preferences.
As shown in
The operator may then perform an analysis of the intraoral cavity, in block 414, to determine if the constraints of the restoration/restoration material of the optimal restoration option will be met upon installation. The analysis may be performed by, for example, automatic computation based on comparison of volume information from the intra oral scan of block 404 with volume information about the optimal restoration option selected. Upon determining that further tooth material may have to be removed to meet the constraints, the operator may perform a follow up preparation in block 418 and obtain new intraoral scan in block 420. Responsive to determining that the constraints will be fulfilled, the optimal restoration option may be fabricated and installed in the oral cavity (block 416).
With reference to
In one aspect, application 604 may receive characteristics of a plurality of possible treatment plans as input data 602. In another aspect, the machine learning engine 126 may receive the plurality of treatment plans as three-dimensional (3D) images, and identify, by an input resource module 622, the characteristics of the plurality of treatment plans for use as input data to the application 604. Even further, the input resource module 622 may receive the intraoral scan, compute from the intraoral scan the plurality of treatment plans, and compute therefrom characteristics of the plurality of treatment plans. As discussed herein, the characteristics of possible treatment plans 618 may include a tooth number of the defective tooth, a caries susceptibility of the patient, a bruxism grade of the patient (such as one rated on a relative scale of normal, increased, heavy) preferences 620 (such as costs, conservation, durability preference), computed sacrifice to defect ratios (of the plurality of treatment plans including, for example, the plastic filling 520, inlay 522, onlay 524, crown 526), estimated fracture forces (of the plurality of treatment plans including, for example, the plastic filling 520, inlay 522, onlay 524, crown 526). These may further be computed for different material types for each restoration option.
The input data may be used as input for a trained optimal restoration recommendation module 614 and the trained optimal restoration recommendation module 614 may extract, responsive to the identifying, one or more features from the input data, the one or more features representative of a request for completing the recommendation process. The trained optimal restoration recommendation module 614 may then propose at least one optimal restoration option 612 needed to achieve a desired restoration installation taking into consideration specific patient parameters that may be present for one patient and that may be absent for another patient.
The input resource module 622 may be a dental software component that retrieves and/or prepares the characteristics of possible treatment plans 618, from any source such as the intra-oral camera 122 from and/or other sources. The input data 602 may be representative of each of the possible treatment plans and may further include preferences 620 obtained from, for example, a patient profile, a practitioner profile, a group profile, etc. Dependencies 624 such as availability of device types, connected devices, etc. may also be used as input data 602.
Further, individual portions of the input data 602 may be weighted or prioritized to drive corresponding changes in proposals. These dependencies may otherwise be complex, need significant skill when chosen manually and their significance on final user-specific restoration options may not always be clearly understood by every operator. Thus, not taking said dependencies and preferences into account may result in sub-optimal restoration results, processes, and cost.
In an embodiment, the trained optimal restoration recommendation module 614 (trained machine learning model) may be configured to include a feature extraction component that may generate relevant features for a proposal based on data from all the different available features (e.g., characteristics of possible treatment plans 618, preferences 620, dependencies 624). The feature extraction component may be part of the trained optimal restoration recommendation module 614, which may comprise be a deep neural network/m/l model 606 (machine learning model). In other embodiments, the feature extraction component may be a feature selection component and may be separate from the trained optimal restoration recommendation module 614. The feature extraction component may use a defined algorithm of prioritization or dependencies to generate the features for the recommendation of the optimal restoration option 612.
As discussed herein, the trained optimal restoration recommendation module 614 may propose an optimal restoration option 612 which may include a durability of each of the optimal restoration options or a durability of all input restoration options with the optimal durability being highlighted.
The trained optimal restoration recommendation module 614 can be based, for example, on an artificial machine learning neural network such as a convolutional neural network (CNN), though it is not meant to be limiting. It may be a feed-forward artificial neural network which in a classic form may comprise a convolutional layer, followed by a pooling layer. The CNN learns by learning free parameters or classifiers of the convolution kernel per layer and their weighting when calculating the next layer.
A training of the m/l model 606 or trained optimal restoration recommendation module 614 according to an illustrative embodiment is discussed hereinafter.
In an illustrative embodiment, presentation module 608 of application 604 displays the output or optimal restoration option obtained from the trained optimal restoration recommendation module 614. The presentation module 608 may also display, for example, plurality of restoration options ranked from most optimal to least optimal for the specified user. An adaptation module 610 may be configured to receive input from an operator to adapt the optimal restoration option 612 if necessary. For example, changing a material preference may cause recalculation of proposed optimal restoration option 612 for presentation by the presentation module 608.
As new tools and materials with specific instructions and requirements are added to a dental workflow, and as operators continue to make decisions on optimal restoration options 612 proposed for them, the trained optimal restoration recommendation module 614 may be retrained and a need for the practitioner to be intimately aware of all user specific details may be significantly reduced. Feedback module 616 may further collect user feedback 620 indicative of an accuracy of the optimal restoration option 612.
The neural network m/l model 606 may be trained using various types of training data sets. The training may include using a training dataset that comprises training input characteristics of possible treatment plans, preferences and dependencies and corresponding training output optimal restoration options corresponding to the training input. The training input and output may be obtained or computed from a plurality of existing treatment databases or via expert analysis.
By selecting a diverse set of training data 704, the program code trains machine learning model 708 to identify and weight various attributes of patients, practitioners, devices connected to the dental system, etc. To utilize the machine learning model 708, the program code obtains (or derives) input data or features to generate an array of values to input into input neurons of a neural network. Responsive to these inputs, the output neurons of the neural network produce an array that includes the output optimal restoration option that may be presented contemporaneously on a display.
Turning now to
In another aspect, a proximity of a residual tooth substance to a pulp or tooth root may be computed for use in computing an invasiveness score based on at least the proximity. This may further be used as input to the trained optimal restoration recommendation module 614. Further, as part of the output of the trained optimal restoration recommendation module 614, a restoration prognosis comprising a stability score and/or a predicted lifetime may be provided for each possible treatment plan.
In another aspect, at least one tooth number is used as one of the input data 602.
In another aspect, the proposal module may comprise analytical/numerical computation, statistical analysis, and preference filtering components.
Any specific manifestations of these and other similar example processes are not intended to be limiting to the disclosure. Any suitable manifestation of these and other similar example processes can be selected within the scope of the illustrative embodiments.
Thus, a computer-implemented method, system or apparatus, and computer program product are provided in the illustrative embodiments for generating optimal restoration options and other related features, functions, or operations. Where an embodiment or a portion thereof is described with respect to a type of device, the computer-implemented method, system or apparatus, the computer program product, or a portion thereof, are adapted or configured for use with a suitable and comparable manifestation of that type of device.
Where an embodiment is described as implemented in an application, the delivery of the application in a Software as a Service (Saas) model is contemplated within the scope of the illustrative embodiments. In a SaaS model, the capability of the application implementing an embodiment is provided to a user by executing the application in a cloud infrastructure. The user can access the application using a variety of client devices through a thin client interface such as a web browser, or other light-weight client-applications. The user does not manage or control the underlying cloud infrastructure including the network, servers, operating systems, or the storage of the cloud infrastructure. In some cases, the user may not even manage or control the capabilities of the SaaS application. In some other cases, the SaaS implementation of the application may permit a possible exception of limited user-specific application configuration settings.
In the following, various examples for implementing aspects of the disclosure are described:
Example 1. A method comprising:
Example 2. The method of example 1, or any other examples, wherein the minimally invasive procedure includes an excavation of tooth material or a detection of dental caries.
Example 3. The method of example 1, or any other examples, wherein the plurality of treatment plans are selected from the list consisting of a filling, an inlay, an onlay, and a crown.
Example 4. The method of example 3, or any other examples, further comprising:
Example 5. The method of example 1, or any other examples, further comprising:
Example 6. The method of example 5, or any other examples, wherein the characteristics of the plurality of treatment plans are selected from the list consisting of a tooth number, a caries susceptibility, a bruxism grade, a patient or dentist preference, a sacrifice to defect ratio parameter for each treatment plan that represents a volume of tooth material to remove for the treatment plan relative to a volume of the dental defect, and an estimated fracture force for each treatment plan.
Example 7. The method of example 5, or any other examples, wherein the optimal restoration option includes a durability of a treatment plan of the plurality of treatment plans.
Example 8. The method of example 1, or any other examples, wherein the one or more constraints is selected from a list consisting of a minimum wall thickness of the one or more restoration materials, a volume of the residual tooth substance to remove to achieve a treatment plan, an expected masticatory pressure based on tooth position or defect location, and a medical history of a patient.
Example 9. The method of example 1, or any other examples, computing from the intraoral scan, responsive to selecting the optimal restoration option, an extent to which an area of the oral cavity to be restored meets manufacturer processing instructions for the material of the optimal restoration option, and
Example 10. The method of example 1, or any other examples, further comprising:
Example 11. The method of example 1, or any other examples, further comprising:
Example 12. The method of example 1, or any other examples, further comprising:
Example 13. The method of example 1, or any other examples, further comprising:
Example 14. The method of example 1, or any other examples, wherein the generating is further based on a preference selected from the list consisting of a material availability, a cost limit, a maximum invasiveness, a minimum restoration lifetime.
Example 15. The method of example 1, or any other examples, wherein the dental defect is visualized.
Example 16. The method of example 1, or any other examples, wherein the plurality of treatment plans are ordered according to a predefined metric and visualized.
Example 17. The method of example 16, or any other examples, wherein one or more other characteristics of the dental defect are also visualized.
Example 18. The method of example 16, or any other examples, wherein the generating is performed by one or more of machine learning, analytical/numerical computation, statistical analysis, and preference filtering.
Example 19. A system comprising:
Example 20. A non-transitory computer-readable storage medium, including instructions that when executed by a computer, cause the computer to:
The present disclosure may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to carry out aspects of the present disclosure.
The computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer-readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer-readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer-readable program instructions described herein can be downloaded to respective computing/processing devices from a computer-readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing/processing device.
Computer-readable program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer-readable program instructions may execute entirely on a dedicated system or user's computer, partly on the user's computer or dedicated system as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server, etc. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer-readable program instructions by utilizing state information of the computer-readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.
Aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.
All features disclosed in the specification, including the claims, abstract, and drawings, and all the steps in any method or process disclosed, may be combined in any combination, except combinations where at least some of such features and/or steps are mutually exclusive. Each feature disclosed in the specification, including the claims, abstract, and drawings, can be replaced by alternative features serving the same, equivalent, or similar purpose, unless expressly stated otherwise.
