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Dental images processed with artificial intelligence — Richard Ricci (US10937160B1)

Richard Ricci · Google Patents

Google Patents · Patents · License: Open Access

patent, google patents, intellectual property, US10937160B1, Richard Ricci, en, 2021

ABSTRACT

Abstract

Dental images processed with artificial intelligence for e-commerce is described. In an example scenario, a processing device receives a dental image. The dental image is processed with a deep neural network and matched to anatomy and pathology datasets in real time. The matched and identified dental image is matched to a dental treatment recommendation dataset to further produce a real time dental product recommendation for a patient. One or more artificial intelligence entities may provide in real time dental treatment recommendations and/or real time dental product recommendations to a dental professional and/or an individual. The process may also produce a diagnostic treatment aid and/or a product recommendation such as an orthodontic aligner. Artificial intelligence dental datasets may be provided to dental professionals, health care professionals, individuals and e-commerce organizations that may buy, sell, exchange and/or transfer this information over a communication network such as the internet and/or a cell phone.

Description

CLAIM OF PRIORITY

The current application claims a priority to the U.S. Provisional Patent Application Ser. No. 62/955,321 filed on Dec. 30, 2019.

FIELD OF THE INVENTION

The field of the invention relates to a system to provide artificial intelligence processing of at least one of: a dental image, a dental image landmark for e-commerce. The dental image or dental image landmark (received from a source such as an x-ray, a camera, or an image capturing device) may be processed by at least one of: an artificial intelligence system, an artificial intelligence model, an artificial intelligence dataset to produce at least one of: a real time dental treatment recommendation, a real time dental product recommendation for e-commerce.

BACKGROUND OF THE INVENTION

Existing techniques for generating a dental recommendation based on dental image processing are deficient with regard to several aspects. For instance, current technologies do not generate a dental treatment recommendation (such as a root canal) or a dental product recommendation (such as an orthodontic aligner) based on an artificial intelligence model's processing of a dental image or a dental image landmark. Further, current technologies do not address e-commerce transactions of dental treatment recommendations or dental product recommendations between at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device.

Artificial intelligence systems have become routinely incorporated into our daily lives. The use of voice recognition digital assistants is a common interactive activity for many cell phone users. Digital assistant, suggest digital content based on a users prior content history. Facial recognition systems may utilize a combination of structured light and infrared red light with an artificial intelligence algorithm to produce a 3D textured model of a user's face. Users may unlock their cell phones and process financial transactions utilizing facial recognition. Further, facial recognition systems that utilize artificial intelligence and recurrent neural networks can identify and classify a facial image with the same accuracy as a human.

Big data deals with datasets that are too large for traditional data processing. Big data is collectively created from merging a huge volume of data compiled from the internet and is frequently used for predictive business decisions. Big data requires many redundant and complicated mathematical tasks to be delegated to artificial intelligence models. These models can be allowed to run unsupervised and unbiased. Human rules and training may be provided to artificial intelligence models. Artificial intelligence models may use electronic sensors (e.g., computer vision, pressure sensors, smoke detectors and microphones) and human training to teach computers to process the five senses with big data analytics. Techniques such as non-linear regression, exponential powers laws, geometric series, binomial distribution may also be used in analyzing big data analytics. Currently, there is a need for training artificial intelligent models with big data and sensors to generate dental treatment recommendations and dental product recommendations.

Digital medical images have led to vast improvements in patient diagnosis and treatment planning options. Constantly evolving medical image technologies have allowed many redundant and complicated mathematical tasks to be delegated to artificial intelligence models with reactive and limited memory capacities to process big data. Advances in computer science artificial intelligence models have revolutionized medical image diagnostic methods and enable users to manage digital images with extreme speed and accuracy. Unfortunately, dental image diagnosis models and dental image artificial intelligence diagnostic models have not been developed yet and there is a need for the development of these technologies.

SUMMARY OF THE EMBODIMENT

Dental images processed with artificial intelligence for e-commerce is described. In an example scenario, a processing device receives at least one of: a dental image, a dental image landmark. A dental image or a dental image landmark may be matched and identified with a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset with a deep neural network by at least one of: a machine learning system, a deep learning system. At least one deep neural network may learn to detect different dental image landmarks of a dental image and match and identify to a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset to produce a real time confidence score of at least one of: a dental anatomy landmark, a dental pathology landmark and provide to a real time dental image dataset.

At least one of: a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset may be at least one of: obtained, annotated from at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, an aggregator server, an aggregator service, a processing device. The process may continue to match and identify a real time dental image dataset to at least one of: a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset to produce a real time confidence score for a dental treatment recommendation and provide this confidence score to a real time dental treatment recommendation dataset. An aggregate server and/or processing device may match and identify a real time dental image dataset and/or a real time dental treatment recommendation dataset to at least one of: a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset to produce one or more real time confidence scores of a dental product recommendation. These one or more real time confidence scores of a dental product recommendation may be provided to a real time dental product recommendation dataset. An example of a real time dental product recommendation may include recommending a specific orthodontic aligner or a manufacturer for an orthodontic aligner fabrication. Another example of a real time dental product recommendation may include a specific dental implant or a specific dental implant size based on a dental image.

The supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset and a real time correlation dataset are configured to continually merge and correlate additional annotated information from at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device. Further, additional annotated dental images or dental image landmarks may be processed with supervised learning, unsupervised learning, rewards training, transfer learning, confidence values, confidence scores, reactive memory, non reactive memory, a memory of dataset, a system of artificial intelligence with memory. The supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset may be configured to identify and correct for missing information.

An artificial intelligence system may work individually or as a team in any order or combination. An artificial intelligence system does not necessarily imply sentient intelligence but may act as an interface of human interpretation rules and training that are provided to a single artificial intelligence system or a team of artificial intelligence systems. Further, an artificial intelligence system may be based on human interpretation rules and training.

At least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a machine learning mechanism, a deep learning mechanism may process a transaction of at least one of: an exchange, a transfer, a purchase, a sell of at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset over a communication network such as the internet, wherein a communication network includes at least one of: the internet, an intranet, an extranet, an Internet, an internet transaction service, an online transaction service, a mobile network, a cell phone, a wearable technology, a wireless network, a cloud platform, an online transaction processing (OLTP) service, an online analytical processing (OLAP) service, a transaction platform. Further, a processor configured for a dental professional or an individual to process a transaction of at least one of: an exchange, a transfer, a buy, a sell of at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a correlation dataset in exchange for at least one of: a currency, a data, a discount, a product, a good, a software, an application, an advertisement.

Further, a method of generating a dental recommendation based on image processing in accordance with some embodiments. The method may include receiving, using a processing device or a communication device, at least one patient data comprising at least one image from at least one patient device. The method may include retrieving, using a storage device, at least one dental dataset. The method may include analyzing, using a processing device, at least one patient data and at least one dental dataset. At least one dental dataset is associated with at least one patient data. The at least one dental dataset comprises at least one of a classified dental image anatomy dataset and a classified dental image pathology dataset. The method may include generating, using an processing device, at least one landmark based on an analyzing. At least one landmark comprises at least one dental characteristic associated with at least one patient data. The method may include retrieving, using a storage device, at least one dental reference dataset. The method may include processing, using a device, at least one landmark and at least one dental reference dataset. The method may include determining, using a processing device, at least one dental recommendation based on a processing. The method may include transmitting, using a processing device or a communication device, at least one dental recommendation to at least one external device. The method may include storing, using a storage device, at least one dental recommendation.

According to some embodiments, a system of generating a dental recommendation based on image processing is disclosed. The system may include a processing device or a communication device configured for receiving at least one patient data comprising at least one image from at least one patient device. The processing device or a communication device may be configured for transmitting at least one dental recommendation to at least one external device. The system may include a processing device configured for analyzing at least one patient data and at least one dental dataset. At least one dental dataset is associated with at least one patient data. At least one dental dataset comprises at least one of a classified dental image anatomy dataset and a classified dental image pathology dataset. The processing device may be configured for generating at least one landmark based on an analyzing. At least one landmark comprises at least one dental characteristic associated with at least one patient data. A processing device may be configured for processing at least one landmark and at least one dental reference dataset. A processing device may be configured for determining at least one dental recommendation based on a processing. The system may include a storage device configured for retrieving at least one dental dataset, retrieving at least one dental reference dataset and storing at least one dental recommendation.

Both the foregoing summary and the following detailed description provide examples and are explanatory only. Accordingly, the foregoing summary and the following detailed description should not be considered to be restrictive. Features or variations may be provided in addition to those set forth herein. For example, embodiments may be directed to various feature combinations and sub-combinations described in the detailed description.

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure.

The drawings may contain text or captions that may explain certain embodiments of the present disclosure. This text is included for illustrative, non-limiting, explanatory purposes of certain embodiments detailed in the

CLAIM OF PRIORITY

The current application claims a priority to the U.S. Provisional Patent Application Ser. No. 62/955,321 filed on Dec. 30, 2019.

FIELD OF THE INVENTION

BACKGROUND OF THE INVENTION

SUMMARY OF THE EMBODIMENT

BRIEF DESCRIPTION OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate various embodiments of the present disclosure.

FIG. 1 is an illustration of an online platform consistent with various embodiments of the present disclosure.

FIG. 2 is an illustration diagram of a system for facilitating generating a dental recommendation based on image processing, in accordance with some embodiments.

FIG. 3 is a flowchart of a method of generating a dental recommendation based on image processing, in accordance with some embodiments.

FIG. 4 is a flowchart of a method of obtaining a first confidence score associated with at least one dental recommendation, in accordance with some embodiments.

FIG. 5 is a flowchart of a method of obtaining at least one updated reference dataset, in accordance with some embodiments.

FIG. 6 is a flowchart of a method of processing an order, in accordance with some embodiments.

FIG. 7 is a flowchart of a method of obtaining a second confidence score associated with at least one landmark, in accordance with some embodiments.

FIG. 8 is a flowchart of a method of obtaining a validity notification corresponding to at least one patient data, in accordance with some embodiments.

FIG. 9 is a flowchart of a method of obtaining a notification corresponding to at least one first user, in accordance with some embodiments.

FIG. 10 is a conceptual diagram of a method for facilitating matching and identifying a dental image with a supervised and/or unsupervised annotated deep neural network to produce a real time correlation dataset, in accordance with some embodiments.

FIG. 11 is a conceptual diagram for processing a real time correlation dataset with artificial intelligence systems to produce a polling from at least one of: a first artificial intelligence system, a second artificial intelligence system, a team of artificial intelligence systems to produce at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product.

FIG. 12 is a block diagram of a computing device for implementing the methods disclosed herein, in accordance with some embodiments.

DETAILED DESCRIPTION OF THE INVENTION

The field of the present invention relates to a system for providing artificial intelligence processing of at least one of: a dental image, a dental image landmark for e-commerce. The dental image or dental image landmark, which may be obtained by image capture device, may be processed by at least one of: an artificial intelligence system, an artificial intelligence dataset, an artificial intelligence model to produce at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product for e-commerce. As a preliminary matter, it will readily be understood by one having ordinary skill in the relevant art that the present disclosure has broad utility and application. As should be understood, any embodiment may incorporate only one or a plurality of the above-disclosed aspects of the disclosure and may incorporate only one or a plurality of the above-disclosed features. Any embodiment discussed and identified as being âpreferredâ is considered to be part of a best mode contemplated for carrying out the embodiments of the present disclosure. Other embodiments also may be discussed for additional illustrative purposes in providing a full and enabling disclosure. Moreover, many embodiments, such as adaptations, variations, modifications, and equivalent arrangements, will be implicitly disclosed by the embodiments described herein and fall within the scope of the present disclosure.

Accordingly, while embodiments are described herein in detail in relation to one or more embodiments, it is to be understood that this disclosure is illustrative and exemplary of the present disclosure, and are made merely for the purposes of providing a full and enabling disclosure. The detailed disclosure herein of one or more embodiments is not intended, nor is to be construed, to limit the scope of patent protection afforded in any claim of a patent issuing here from, which scope may be defined by the claims and the equivalents thereof.

Thus, for example, any sequence(s) and/or temporal order of steps of various processes or methods that are described herein are illustrative and not restrictive. Accordingly, it should be understood that, although steps of various processes or methods may be shown and described as being in a sequence or temporal order, the steps of any such processes or methods are not limited to being carried out in any particular sequence or order and may be omitted in any sequence or order. Indeed, the steps in such processes or methods generally may be carried out in various different sequences and orders while still falling within the scope of the present disclosure.

Additionally, it is important to note that each term used herein refers to that which an ordinary artisan would understand such term to mean based on the contextual use of such term herein. To the extent that the meaning of a term used herein, as understood by the ordinary artisan based on the contextual use of such term, differs in any way from any particular dictionary definition of such term, it is intended that the meaning of the term as understood by the ordinary artisan should prevail.

It is important to note that, as used herein, âaâ and âanâ each generally denotes âat least one,â but does not exclude a plurality unless the contextual use dictates otherwise. When used herein to join a list of items, âorâ denotes âat least one of the items,â but does not exclude a plurality of items of the list. Finally, when used herein to join a list of items, âandâ denotes âall of the items of the list.â

The following detailed description refers to the accompanying drawings. Wherever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While many embodiments of the disclosure may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting, reordering, or adding stages to the disclosed methods. Accordingly, the following detailed description does not limit the disclosure. Instead, the proper scope of the disclosure is defined by the claims found herein and/or issuing here from. The present disclosure contains headers. It should be understood that these headers are used as references and are not to be construed as limiting upon the subjected matter disclosed under the header.

The present disclosure includes many aspects and features. Moreover, while many aspects and features relate to, and are described in the context of methods and systems of generating a dental recommendation based on image processing, embodiments of the present disclosure are not limited to use only in this context.

In general, the method disclosed herein may be performed by one or more computing devices. For example, in some embodiments, the method may be performed by a server computer in communication with one or more client devices over a communication network such as, for example, the internet. In some other embodiments, the method may be performed by one or more of at least one of: one server computer, one client device, one network device, one sensor. Examples of one or more client devices and/or a server computer may include, a desktop computer, a laptop computer, a tablet, a personal digital assistant, a portable electronic device, a wearable computer, a smartphone, an Internet of Things (IoT) device, a smart electrical appliance, a video game console, a rack server, a super-computer, a mainframe computer, a mini-computer, a micro-computer, a storage server, an application server, (a mail server, a web server, a real-time communication server, an FTP server, a virtual server, a proxy server, a DNS serve). One or more client devices and/or a server computer may be configured for executing a software application such as, for example, but not limited to, an operating system (e.g., Windows, macOS, Unix, Linux, Android) in order to provide a user interface (e.g., GUI, touch-screen based interface, voice-based interface, gesture-based interface) for use by one or more users and/or a network interface for communicating with other devices over a communication network. Accordingly, a processing device may include a processing device configured for performing data processing tasks such as, for example, but not limited to, analyzing, identifying, determining, generating, transforming, calculating, computing, compressing, decompressing, encrypting, decrypting, scrambling, splitting, merging, interpolating, extrapolating, redacting, anonymizing, encoding and decoding. A processing device may include a communication device configured for communicating with one or more external devices. One or more external devices may include, for example, but are not limited to, a client device, a third-party database, a public database, a private database. The processing device and/or a communication device may be configured for communicating with one or more external devices over one or more communication channels. One or more communication channels may include a wireless communication channel and/or a wired communication channel. Accordingly, a processing device and/or a communication device may be configured for performing transmitting and/or receiving of information in electronic form. A server computer may include a storage device configured for performing data storage and/or data retrieval operations. In general, a storage device may be configured for providing reliable storage of digital information. Accordingly, in some embodiments, a storage device may be based on technologies such as, but not limited to, data compression, data backup, data redundancy, deduplication, error correction, data finger-printing, role-based access control.

One or more steps of the method disclosed herein may be initiated, maintained, controlled and/or terminated based on a control input received from one or more devices operated by one or more users such as, for example, but not limited to, an end-user, an administrator, a service provider, a service consumer, an agent, a broker and a representative thereof. The user as defined herein may refer to a human, and/or an artificially intelligent system in any state of existence, unless stated otherwise, elsewhere in the present disclosure. In some embodiments, one or more users may be required to successfully perform authentication in order for the control input to be effective. In general, a user of the one or more users may perform authentication based on the possession of a secret human-readable secret data (e.g., username, password, passphrase, PIN, secret question, secret answer) and/or possession of a machine-readable secret data (e.g., encryption key, decryption key, bar codes) and/or possession of one or more embodied characteristics unique to the user (e.g., biometric variables such as but not limited to, fingerprint, palm-print, voice characteristics, behavioral characteristics, facial features, iris pattern, heart rate variability, evoked potentials, brain waves) and/or possession of a unique device (e.g., a device with a unique physical and/or chemical and/or biological characteristic, a hardware device with a unique serial number, a network device with a unique IP/MAC address, a telephone with a unique phone number, a smartcard with an authentication token stored thereupon). Accordingly, one or more steps of the method may include communicating via transmitting and/or receiving with one or more sensor devices and/or one or more actuators in order to perform authentication. For example, one or more steps may include receiving, using a processing device and/or a communication device, a secret human-readable data from an input device such as a keyboard, a keypad, a touch-screen, a microphone, a camera. Likewise, one or more steps may include receiving, using a processing device and/or a communication device, one or more embodied characteristics from one or more biometric sensors.

One or more steps of the method may be automatically initiated, maintained and/or terminated based on one or more predefined conditions. In an instance, one or more predefined conditions may be based on one or more contextual variables. In general, one or more contextual variables may represent a condition relevant to the performance of one or more steps of the method. One or more contextual variables may include, but are not limited to, location, time, identity of a user associated with a device (e.g., the server computer, a client device) corresponding to the performance of one or more steps, environmental variables (e.g., temperature, humidity, pressure, wind speed, lighting, sound) associated with a device corresponding to the performance of one or more steps, physical state and/or physiological state and/or psychological state of the user, physical state (e.g., motion, direction of motion, orientation, speed, velocity, acceleration, trajectory) of the device corresponding to the performance of one or more steps and/or semantic content of data associated with one or more users. Accordingly, one or more steps may include communicating with one or more sensors and/or one or more actuators associated with one or more contextual variables. For example, one or more sensors may include, but are not limited to, a timing device (e.g., a real-time clock), a location sensor (e.g., a GPS receiver, a GLONASS receiver, an indoor location sensor), a biometric sensor (e.g., a fingerprint sensor), an environmental variable sensor (e.g., temperature sensor, humidity sensor, pressure sensor) and a device state sensor (e.g., a power sensor, a voltage/current sensor, a switch-state sensor, a usage sensor-associated with the device corresponding to performance of one or more steps).

One or more steps of the method may be performed one or more number of times. Additionally, one or more steps may be performed in any order other than as exemplarily disclosed herein, unless explicitly stated otherwise elsewhere in the present disclosure. Two or more steps of the one or more steps may, in some embodiments, be simultaneously performed, at least in part. In some embodiments, there may be one or more time gaps between the performance of any two steps of the one or more steps. Further, at least one of: a processing device, a processing service, an aggregator, aggregator server, a communication device is configured to at least one of: execute an instruction in any order, omit an instruction in any order. Wherein an instruction may include one of: a match, an identify, a score, a rule, a train, a process, an exchange, a transfer, a purchase, a sell, a receive, a retrieve, a transmit, an analyze, a generate, a transmit, an order.

In some embodiments, one or more predefined conditions may be specified by one or more users. Accordingly, one or more steps may include receiving, using a processing device and/or a communication device, one or more predefined conditions from one or more and devices operated by one or more users. One or more predefined conditions may be stored in a storage device. Alternatively, and/or additionally, in some embodiments, one or more predefined conditions may be automatically determined, using a processing device, based on historical data corresponding to performance of one or more steps. For example, the historical data may be collected, using a storage device, from a plurality of instances of performance of the method. Such historical data may include performance actions (e.g., initiating, maintaining, interrupting, terminating) of one or more steps and/or one or more contextual variables associated therewith. Machine learning and/or deep learning may be performed on the historical data in order to determine one or more predefined conditions. For instance, machine learning of historical data may determine a correlation between one or more contextual variables and performance of one or more steps of the method. Accordingly, the one or more predefined conditions may be generated, using a processing device, based on a correlation.

One or more steps of the method may be performed at one or more spatial locations. For instance, the method may be performed by a plurality of devices interconnected through a communication network. Accordingly, in an example, one or more steps of the method may be performed by a server computer. Similarly, one or more steps of the method may be performed by a client computer. Likewise, one or more steps of the method may be performed by an intermediate entity such as, for example, a proxy server. For instance, one or more steps of the method may be performed in a distributed fashion across the plurality of devices in order to meet one or more objectives. For example, one objective may be to provide load balancing between two or more devices. Another objective may be to restrict a location of one or more of an input data, an output data and any intermediate data there between corresponding to one or more steps of the method. For example, in a client-server environment, sensitive data corresponding to a user may not be allowed to be transmitted to a server computer. Accordingly, one or more steps of the method operating on a sensitive data and/or a derivative data thereof may be performed on the client device.

FIG. 1 is an illustration of an online platform 100 consistent with various embodiments of the present disclosure. By way of non-limiting example, the online platform 100 to facilitate generation of a dental recommendation based on image processing may be hosted on a centralized server 102 , such as, for example, a cloud computing service. The centralized server 102 may communicate with other network entities, such as, for example, a mobile device 106 (such as a smartphone, a laptop, a tablet computer), electronic devices 110 (such as desktop computers, server computers), databases 114 , sensors 116 , over a communication network 104 , such as, but not limited to, the internet. Users of the online platform 100 may include relevant parties such as, but not limited to, end users, and administrators. Accordingly, in some instances, electronic devices operated by the one or more relevant parties may be in communication with the online platform 100 . A user 112 , such as one or more relevant parties, may access online platform 100 through a web based software application or browser. A web based software application may be embodied as, for example, but not be limited to, a website, a web application, a desktop application, and a mobile application compatible with a computing device 1200 .

FIG. 2 is an illustration diagram of a system 200 for generating a dental recommendation based on image processing, in accordance with some embodiments. Accordingly, system 200 may include a communication device 202 , a processing device 204 and a storage device 206 . A communication device 202 may be configured for receiving at least one patient data comprising at least one image from at least one patient device 208 . A communication device 202 may be configured for transmitting at least one dental recommendation to at least one external device 210 . At least one patient data may include an individual information dataset and/or a personal information associated with at least one patient. At least one of: one patient data, an individual information dataset, a personal information may include at least one of: an age, a first name, a gender, a middle initial, a middle name, a last name, a sex, a date of birth, a zip code, an address, a geographic location, a cell phone number, a telephone number, a current medication, a previous medication, a social security number, a marital status, an insurance, an insurance identification number, an email address, internet protocol address, a change of insurance, an employer, a change of employment, a change of zip code, a change of the previous medication, a change of a marital status, a change of gender, a location, a chance of location, a biometric measurement, a biometric sensor measurement, a genetic dataset, an internet browsing history, a dataset. At least one patient device 208 may include at least one user device and at least one diagnosis device, wherein at least one user device is associated with at least one patient, wherein at least one diagnosis device is configured for generating a diagnosing data. At least one user device may include a smartphone, a tablet, a mobile, a personal computer, a laptop. At least one diagnosing device may include an x-ray, a camera, a toothbrush with imaging device, toothbrush with imaging device a being camera, an intraoral scanner, a magnetic resonance image (MRI) device, a computed tomography (CT) scan equipment, cone beam computed tomography (CBCT) device, a scintillator technology based imaging machine, a trans-illumination imaging machine, a fluorescence technology based imaging machine, a blue fluorescence technology based imaging machine, a laser based technology based imaging machine, an indirect or direct flat panel detector (FPD), a charged couple device (CCD), a phosphor plate radiography device, a picture archiving and communication system (PACS), a photo-stimulable phosphor (PSP) device, a wireless complementary metal-oxide-semiconductor (CMOS) device. In some embodiments, at least one external device 210 may be associated with the at least one patient device 208 . At least one external device 210 may include a smartphone, a tablet, a mobile, a personal computer, a laptop. In some embodiments, the at least one external device 210 may be associated with a second user. A second user may include a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device. A processing device 204 may be configured for analyzing at least one patient data and at least one dental dataset. At least one dental dataset may be associated with the at least one patient data. At least one dental dataset may include at least one of a classified dental image anatomy dataset and a classified dental image pathology dataset.

In some embodiments, the analyzing of at least one patient data and at least one dental dataset may be based on at least one first artificial intelligence model. At least one first artificial intelligence model may be configured for identifying of at least one dental characteristic associated with at least one patient data. At least one first intelligence model comprises at least one of a deep neural network model, a machine learning model, a recurrent neural network model, a deep forest decision tree, and an independent neural network model. At least one first artificial intelligence model may include a computer vision model.

In an embodiment, at least one first artificial intelligence model may be trained based on a confidence score associated with at least one image. A confidence score may be provided by at least one expert. At least one first artificial intelligence model may be trained for identifying of at least one dental characteristic. A processing device 204 may be configured for generating at least one landmark based on analyzing. At least one landmark comprises at least one dental characteristic associated with the at least one patient data. The processing device 204 may be configured for processing at least one landmark and at least one dental reference dataset. Additionally, a processing device 204 may be configured for determining at least one dental recommendation based on a processing.

In some embodiments, processing of at least one landmark and at least one dental reference may be based on at least one second artificial intelligence model. At least one second artificial intelligence model may be configured for determining of at least one dental recommendation. At least one second intelligence model comprises at least one of a deep neural network model, a machine learning model, a recurrent neural network model, a deep forest decision tree, and an independent neural network model. At least one second artificial intelligence model may include a computer vision model.

In an embodiment, at least one second artificial intelligence model may be trained based on a confidence score associated with at least one image. The confidence score may be provided by at least one expert. At least one second artificial intelligence model may be trained for determining of at least one dental recommendation. A storage device 206 may be configured for retrieving at least one dental dataset, retrieving at least one dental reference dataset and storing at least one dental recommendation.

In an embodiment, at least one second artificial intelligence model may be trained based on at least one of: a dental image dataset, a dental image treatment dataset, a dental product dataset, a dental image landmark dataset and the an artificial intelligence dataset. Additionally, at least one first artificial intelligence model may be trained based on at least one of: a dental image dataset, a dental image treatment dataset, a dental product dataset, a dental image landmark dataset, an artificial intelligence dataset. In an embodiment, a communication device 202 is configured for transmitting a dental professional referral to the at least one external device 210 . A processing device 204 is configured for analyzing at least one of: a provider dataset, at least one image, a dental treatment data, a dental image treatment dataset, a dental product dataset, a dental image landmark dataset, and the artificial intelligence dataset to generate a dental professional referral, wherein a provider dataset may include a dental professional, a dental specialist, a health care professional, a health care specialist, a dentist, a dental specialist, a hygienist, a dental assistant, a dental staff member, a dental laboratory technician, a physician, a physician specialist, a nurse, a medical technician, an veterinarian, a veterinarian professional.

According to some embodiments, at least one external device 210 may include at least one first user device associated with at least one first user. At least one first user may include a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device. A dental professional may include a dentist, a hygienist, a dental assistant, a dental staff member, a medical staff member, a dental laboratory technician, a physician, a nurse, a medical technician, a veterinarian, a veterinarian professional, a licensed professional. An e-commerce organization may include a business, a business entity, a business owner, an employer, a wholesaler, a retailer, a group, a research entity, a law enforcement entity, a public administration entity, a government agency, an administrator, an administrator entity, a governing agency, a bioinformatics service, an insurance company, a cloud based storage service. At least one first user device may include a smartphone, a tablet, a laptop, a personal computer. A communication device 202 may be configured for transmitting a request to at least one first user device of at least one external device 210 , receiving at least one first user data from at least one first user device; and transmitting a notification to at least one patient device 208 and at least one external device 210 . At least one first user data may include certifications, qualification proof, license number that may be verified. A processing device 204 may be configured for analyzing at least one first user data based on at least one regulatory data and generating the notification corresponding to at least one first user based on the analyzing of at least one first user data. A storage device 206 may be configured for retrieving at least one regulatory data based on at least one first user.

According to some embodiments, a communication device 202 is configured for receiving a transaction request from at least one external device 210 , transmitting a transaction request to at least one first user device, receiving a transaction response from at least one first user device, and transmitting a transaction response to at least one external device 210 . A transaction request may include a request for a transaction of at least one of: a one patient data, a dental image dataset, a dental image treatment dataset, a dental product dataset, at least one dental dataset, a dental image landmark dataset. A transaction may include at least one of: business to business (B2B), business to consumer (B2C), consumer to business (C2B), consumer to consumer (C2C), business to administration (B2A), and consumer to administration (C2A) in exchange for at least one of: a currency, a data, a discount, a product, a good, a software, an application, an advertisement.

In an embodiment, at least one of: one image, one landmark, one patient data, a dental image dataset, a dental image treatment dataset, a dental product dataset, at least one dental dataset, a dental image landmark dataset is configured to compensate for distorted and/or missing image information associated with at least one image. At least one dental recommendation may include at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product.

A communication device 202 may be configured for transmitting at least one dental recommendation and at least one patient data to at least one of: an expert device, a processing device, a patient device associated with at least one expert. At least one expert may include a dental professional, a healthcare professional. A communication device 202 may be configured for receiving a first confidence score from at least one of: an expert device, a processing device, a patient device. A first confidence score may be associated with at least one dental recommendation. A communication device 202 may be configured for transmitting a first confidence score to at least one external device 210 . A first confidence score may include a measure of appreciation. A first confidence score may include a rating, a score. At least one of: an expert device, a processing device, a patient device may include a smartphone, a tablet, a laptop, a personal computer, a server, a desktop computer, a workstation, a laptop computer, a cell phone, a tablet, a mobile device, a cloud based storage service. A real time dental product recommendation may include at least one of: no dental product, at least one dental product, an orthodontic aligner, a dental implant, a crown, a membrane, a graft, a screw, a composite, an amalgam, a dental material, a preventive material, an impression material, a dental instrument.

At least one dental recommendation may include a treatment option aid and/or a treatment demonstration aid. In an embodiment, a <figure-callout id="204" label="processing device" filenames="US10937160-20210302-D000

CLAIMS

Claims ( 20 )

The invention claimed is:

1. A system of generating a dental recommendation for e-commerce based on image processing,

the system comprising: a processing device, wherein the processing device uses a microprocessor is configured to:

receive at least one of: a dental image, a dental image landmark from an e-commerce organization;

wherein the processing device is configured to execute an instruction in any order;

wherein an instruction is at least one of: a match, an identify, a score, a rule, a train, a process, an exchange, a transfer, a purchase, a sell;

train the processing device to use at least one of: machine learning, deep learning to match and identify at least one of: a dental image, a dental image landmark to at least one of: a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset to produce at least one of: a first real time confidence score of at least one of: a dental anatomy landmark, a dental pathology landmark, a second real time confidence score of at least one of: a dental anatomy landmark, a dental pathology landmark, a multiple real time confidence score of at least one of: a dental anatomy landmark, a dental pathology landmark and provide to a real time dental image dataset;

format the real time dental image dataset based on a transaction processed by the e-commerce organization: merge the real time dental image dataset with a transaction processed by the e-commerce organization into the real time correlation dataset; and identify and correct a discrepancy between the real time dental image dataset with a transaction processed by the e-commerce organization and the real time correlation dataset;

train the processing device to use at least one of: machine learning, deep learning to match and identify a real time dental image dataset to at least one of: a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset to produce at least one of: a first real time confidence score for a dental treatment recommendation, a second real time confidence score for a dental treatment recommendation, a multiple real time confidence score for a dental treatment recommendation and provide to a real time dental treatment recommendation dataset, wherein a real time dental treatment recommendation dataset contains at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment;

train the processing device to use at least one of: machine learning, deep learning to match and identify at least one of: a real time dental image dataset, a real time dental treatment recommendation dataset to at least one of: a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset to produce at least one of: a first real time confidence score for a dental product recommendation, a second real time confidence score for a dental product recommendation, a multiple real time confidence score for a dental product recommendation and provide to a real time dental product recommendation dataset, wherein a real time dental product recommendation dataset contains at least one of: a real time dental product recommendation, a real time dental product recommendation for no product;

correlate at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, a biometric dataset with an individual information dataset, wherein an individual information dataset includes at least one of: an age, a first name, a gender, a middle initial, a middle name, a last name, a sex, a date of birth, a zip code, an address, a geographic location, a cell phone number, a telephone number, a current medication, a previous medication, a social security number, a marital status, an insurance, an insurance identification number, an email address, internet protocol address, a change of insurance, an employer, a change of employment, a change of zip code, a change of the previous medication, a change of a marital status, a change of gender, a location, a chance of location, a biometric measurement, a biometric sensor measurement, a genetic dataset, an internet browsing history, a dataset and provide to a real time correlation dataset;

provide a plurality of human interpretation rules based on at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset and provide to at least one of: a first artificial intelligence system, a second artificial intelligence system, a team of artificial intelligence systems;

train to associate, with human interpretation rules, a biometric sensor measurement from at least one of: a vision sensor, a sound sensor, a touch sensor, a smell sensor, a taste sensor, a biometric sensor, a sensor with an individual information dataset to produce a biometric dataset and provide to at least one of: an artificial intelligence system, an artificial intelligence dataset, an artificial intelligence model to select at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, real time a dental product recommendation, a real time dental product recommendation for no product based on at least one of: a dental image, a dental image landmark and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device;

train with human interpretation rules at least one of: a first artificial intelligence system, a second artificial intelligence system, a team of artificial intelligence systems to select at least one of: a real time dental image dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset based on at least one human interpretation rule being met and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device;

based on at least one of: a human interpretation rule, a training being met generate a real time confidence score poll from at least one of: a first artificial intelligence system, a second artificial intelligence system, a team of artificial intelligence systems to execute at least one of: a selection, an instruction of at least one of: a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product based on at least one of: a dental image, a dental image landmark and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device;

process a transaction from at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device of at least one of: an exchange, a transfer, a purchase, a sell of a dataset wherein a dataset includes at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset, over a communication network, wherein a communication network includes at least one of: the internet, an intranet, an extranet, an internet, an internet transaction service, an online transaction service, a mobile network, a cell phone, a wearable technology, a wireless network, a cloud platform, an online transaction processing (OLTP) service, an online analytical processing (OLAP) service, a transaction platform and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device.

2. The processing device of claim 1 , wherein the processing device is configured to process a transaction of at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset with at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device; wherein a transaction includes at least one of: business to business (B2B), business to consumer (B2C), consumer to business (C2B), consumer to consumer (C2C), business to administration (B2A), consumer to administration (C2A) transactions.

3. The processing device of claim 1 , wherein the at least one of: a dental image, a dental image landmark is obtained from at least one of: a digital x-ray, a digital image, a cell phone captured image, a photographic image, a toothbrush with an imaging device, a toothbrush with an imaging device being a camera, a film based x-ray, a digitally scanned x-ray, a digitally captured x-ray, an intraoral scanner, a scintillator technology based image, a trans-illumination image, a fluorescence technology based image, a blue fluorescence technology based image, a laser based technology based image, a magnetic resonance image (MRI), a computed tomography (CT) scan based image, a cone beam computed tomography (CBCT) image.

4. The processing device of claim 1 , wherein at least one of: a dental professional, a health care professional, an expert, an artificial intelligence system, an individual, an e-commerce organization, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device utilizes at least one of: an image capture device, a data storage device; wherein the image capture device includes at least one of: an image capture device, an x-ray equipment, a digital camera, a cell phone camera, an intraoral scanner, a scintillator counter, an indirect or direct flat panel detector (FPD), a charged couple device (CCD), a phosphor plate radiography device, a picture archiving and communication system (PACS), a photo-stimulable phosphor (PSP) device, a wireless complementary metal-oxide-semiconductor (CMOS) device.

5. The processing device of claim 1 , wherein at least one of: a dental image, a dental image landmark includes processing with at least one of:

a machine learning process, a deep learning process, a computer vision component, a sliding window component, a multiple grid component, a bounding box component, an image classification component configured to generate a dental image confidence score, an object classification component configured to generate a dental object confidence score, a value mechanism component configured to generate a treatment confidence score, a value mechanism component configured to generate a dental product confidence score, a value mechanism component configured to generate a real time dental treatment recommendation, a value mechanism component configured to generate a real time dental product recommendation, a semantic segmentation component configured to generate a semantic segmentation, an instance segmentation component configured to generate an instance segmentation, a supervised learning component configured to annotate, an unsupervised learning component configured to annotate, a recurrent neural network component (RNN) configured to analyze a dataset, an independent neural network component (INDRNN) configured to analyze a dataset, a deep forest decision tree configured to analyze a dataset, a processor configured for a system of memory of dataset, a processor configured for an artificial intelligence system with memory, a processor configured for a system of reactive memory, a processor configured for a system of non reactive memory, a processor configured for a system of rewards training, a processor configured for a system of transfer learning, a processor configured with an object tracking mechanism configured to track object, a processor configured for natural language processing (NLP), a processor configured to use non-linear regression, a processor configured to use exponential powers laws, a processor configured to use a geometric series, a processor configured to use a binomial distribution;

a processor configured to at least one of: an upload, a download at least one of: an exchange, a transmission, a storage of a dataset with at least one of: a server, a desktop computer, a workstation, a laptop computer, a cell phone, a tablet, a mobile device, a cloud based storage service;

process a transaction of at least one of: an exchange, a transfer, a buy, a sell at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset; wherein a transaction is at least one of: business to business (B2B), business to consumer (B2C), consumer to business (C2B), consumer to consumer (C2C), business to administration (B2A), consumer to administration (C2A) in exchange for at least one of: a currency, a data, a discount, a product, a good, a software, an application software, an advertisement.

6. The processing device of claim 1 , wherein the processing device is further configured to use at least one of: machine learning, deep learning of at least one of: an electronic sensor measurement, a biometric sensor measurement, a sensor to at least one of: correlate, associate to an individual information dataset to at least one of: choose, select at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product based on at least one of: a dental image, a dental image landmark;

wherein, at least one of: an artificial intelligence system, an artificial intelligence dataset, an artificial intelligence model is configured to learn human interpretation rules of a real time correlation dataset and at least one of: an electronic sensor measurement, a biometric sensor measurement, a sensor measurement to at least one of: correlate, associate to an individual information dataset to at least one of: choose, select at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product based on at least one of: a dental image, a dental image landmark.

7. The processing device of claim 1 , wherein at least one of: a dental image, a dental image landmark, a dataset is configured to compensate for at least one of: a distorted information, a missing image information;

wherein the processing device is configured to execute an instruction in any order;

wherein the processing device is configured to omit an instruction in any order;

wherein an instruction is at least one of: a match, an identify, a score, a rule, a train, a process, an exchange, a transfer, a purchase, a sell.

8. The processing device of claim 1 , wherein at least one of: a dental image, a dental image landmark is processed with at least one deep neural network layer configured to match and identify at least one of:

a dental image anatomy, a dental image pathology, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a real time correlation dataset, a sensor measurement, a biometric dataset.

9. The processing device of claim 1 , wherein at least one of: a dental professional, a health care professional is at least one of: a dentist, a hygienist, a dental assistant, a dental staff member, a medical staff member, a dental laboratory technician, a physician, a nurse, a medical technician, a veterinarian, a veterinarian professional, a licensed professional;

wherein an expert is at least one of: an expert, a dentist, a hygienist, a dental assistant, a dental staff member, a dental laboratory technician, a physician, a nurse, a medical staff member, a medical technician;

wherein an individual is at least one of: an individual, a guardian, an employee;

wherein an e-commerce organization is at least one of: a business, a business entity, a business owner, an employer, a wholesaler, a retailer, a group, a research entity, a law enforcement entity, a public administration entity, a government agency, an administrator, an administrator entity, a governing agency, a bioinformatics service, an insurance company, a cloud based storage service;

wherein an artificial intelligence system is at least one of: an artificial intelligence system, an artificial intelligence model, an artificial intelligence dataset;

wherein a researcher is at least one of: a researcher, a teacher, a professor, an educator, an academic professional, a scientist, a statistician;

wherein a client device is at least one of: a server, a desktop computer, a workstation, a laptop computer, a cell phone, a tablet, a mobile device, a cloud based storage service.

10. The processing device of claim 1 , wherein at least one dataset is configured to produce at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product;

process and display at least one of: a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product as at least one of: a number, a percentage, a percent, a proportion, a ratio, a graph, a color, an image, a score, a grade, a count, a rate, an average, a figure, an outline, an area, a shading;

process with at least one of: a provide, a store at least one of: a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product to at least one of: an application software, a client device, wherein a client device includes at least one of: a server, a desktop computer, a workstation, a laptop computer, a cell phone, a tablet, a mobile device, a cloud based storage service;

provide at least one of: a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product with an individual information dataset to at least one of: a financing organization, a dental specialist, a medical specialist, a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device as a transaction of at least one of: an exchange, a transfer, a buy, a sell over at least one of: a communication network, a cell phone.

11. The processing device of claim 1 , wherein the aggregate server is configured to provide at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product, wherein at least one of: a real time dental treatment recommendation dataset, a real time dental treatment recommendation includes at least one of: no treatment, at least one treatment, a restorative treatment, a prosthodontic treatment, a periodontal treatment, an endodontic treatment, a surgical treatment, a pediatric treatment, an orthodontic treatment;

wherein at least one of: a real time dental product recommendation dataset, a real time dental product recommendation includes at least one of: no product, at least one dental product, an orthodontic aligner, a dental implant, a crown, a membrane, a graft, a screw, a composite, an amalgam, a cement, a luting agent, a gutta percha, a restorative material, an endodontic restorative material, a temporary restorative material, an antimicrobial agent, an antibiotic agent, a medication, a preventive product, a preventive material, an impression material, a dental instrument.

12. A processing device for providing a dental recommendation for e-commerce based on image processing, the processing device comprising:

a computer vision component configured to analyze at least one of: a dental image, a dental image landmark;

a memory configured to store instructions associated with an aggregator,

a processor configured to at least one of: a unidirectional, a bidirectional exchange a dataset with at least one of: a processing device, a mobile device, a cell phone, a client device, a processor;

a processor coupled to the computer vision component and the memory, the processor executing the instructions associated with an aggregator,

wherein an aggregator includes:

an image processing engine configured to process at least one of:

receive at least one of: a dental image, a dental image landmark from an e-commerce organization;

wherein an aggregator is configured to execute an instruction in any order,

wherein the aggregator is configured to omit an instruction in any order,

wherein an instruction is at least one of: a match, an identify, a score, a rule, a train, a process, an exchange, a transfer, a purchase, a sell;

identify and correct a discrepancy between at least one of: a dental image, a dental image landmark;

match and identify at least one of: a dental image, a dental image landmark to a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset to generate a real time confidence score of at least one of: a dental anatomy landmark, a dental pathology landmark and provide to a real time dental image dataset;

process a transaction from the e-commerce organization based on a formatted real time dental image dataset;

merge the real time dental image dataset with a processed transaction from the e-commerce organization into the real time correlation dataset;

and identify and correct a discrepancy between the real time correlation dataset and the e-commerce organization;

match and identify a real time dental image dataset to at least one of: a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset to generate a real time confidence score for a dental treatment recommendation and provide to a real time dental treatment recommendation dataset, wherein a real time dental treatment recommendation dataset contains at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment;

match and identify at least one of: a real time dental image dataset, a real time dental treatment recommendation dataset to at least one of: a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset to generate a real time confidence score for a dental product recommendation and provide to a real time dental product recommendation dataset, wherein a real time dental product recommendation dataset contains at least one of: a real time dental product recommendation, a real time dental product recommendation for no product;

correlate at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, a biometric dataset to an individual information dataset, wherein an individual information dataset includes at least one of: an age, a first name, a gender, a middle initial, a middle name, a last name, a sex, a date of birth, a zip code, an address, a geographic location, a cell phone number, a telephone number, a current medication, a previous medication, a social security number, a marital status, an insurance, an insurance identification number, an email address, internet protocol address, a change of insurance, an employer, a change of employment, a change of zip code, a change of the previous medication, a change of a marital status, a change of gender, a location, a chance of location, a biometric measurement, a biometric sensor measurement, a genetic dataset, an internet browsing history, a dataset and provide to a real time correlation dataset;

provide at least one of: an individual information dataset, a real time correlation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product to a referral system and provide to at least one of: a dental professional, a dental specialist, a health care professional, a health care specialist, a dentist, a dental specialist, a hygienist, a dental assistant, a dental staff member, a medical staff member, a dental laboratory technician, a physician, a physician specialist, a nurse, a medical technician, a veterinarian, a veterinarian professional, a licensed professional;

generate a demonstration aid of at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device;

train to associate, with human interpretation rules, a biometric sensor measurement from at least one of: a vision sensor, a sound sensor, a touch sensor, a smell sensor, a taste sensor, a biometric sensor, a sensor with an individual information dataset to produce a biometric dataset and provide to at least one of: an artificial intelligence system, an artificial intelligence dataset, an artificial intelligence model to select at least one of: a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation, a real time dental product recommendation for no product based on at least one of: a dental image, a dental image landmark and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device;

based on at least one of: a human interpretation rule, a training being met generate a real time confidence score poll from at least one of: a first artificial intelligence system, a second artificial intelligence system, a team of artificial intelligence systems to select and instruct at least one of: a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product based on at least one of: a dental image, a dental image landmark and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device;

process a transaction of at least one of: an exchange, a transfer, a buy, a sell with at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset over a communication network, wherein a communication network includes at least one of: the internet, an intranet, an extranet, an internet, an internet transaction service, an online transaction service, a mobile network, a cell phone, a wearable technology, a wireless network, a cloud platform, an online transaction processing (OLTP) service, an online analytical processing (OLAP) service, a transaction platform and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, a researcher, a manufacturer, a business, an application software, a system software, a client device, a processing device.

13. The processing device of claim 12 , wherein at least one of: a dental professional, a health care professional is at least one of: a dentist, a hygienist, a dental assistant, a dental staff member, a medical staff member a dental laboratory technician, a physician, a nurse, a medical technician, a veterinarian, a veterinarian professional, a licensed professional;

wherein an individual is at least one of: an individual, a guardian, an employee;

wherein an artificial intelligence system is at least one of: an artificial intelligence system, an artificial intelligence model, an artificial intelligence dataset;

âwherein a researcher is at least one of: a researcher, a teacher, a professor, an educator, an academic professional, a scientist, a statistician.

14. The processing device of claim 12 , wherein an individual information dataset may be provided to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, a researcher, a manufacturer, a business, an application software, a patient portal, a system software, a client device, a processing device upon a process to:

verify a compliance of an individual with a regulatory policy;

verify an authorization by the individual to analyze at least one of: a dental image, a dental image landmark, an individual information dataset, a real time correlation dataset, a dataset;

generate, using a processing device, a validity notification corresponding to at least one of: a real time correlation dataset, one patient data based on an analyzing, wherein a validity notification is associated with a measure of approval of an individual;

authenticate an individual to process a transaction of at least one of: an exchange, a transfer, a buy, a sell of a real time correlation dataset for at least one of: a currency, a data, a discount, a product, a good, a software, an application, an advertisement.

15. The processing device of claim 12 , where at least one of: a first artificial intelligence system, a second artificial intelligence system, a team of artificial intelligence systems are configured to at least one of: select, instruct at least one of: a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product based on at least one of: a dental image, a dental image landmark, a supervised annotated dental anatomy landmark dataset, an unsupervised annotated dental anatomy landmark dataset, a supervised annotated dental pathology landmark dataset, an unsupervised annotated dental pathology landmark dataset, a real time dental image dataset, a supervised annotated dental treatment recommendation dataset, an unsupervised annotated dental treatment recommendation dataset, a real time dental treatment recommendation dataset, a real time dental treatment recommendation, a real time dental treatment recommendation for no treatment, a supervised annotated dental product recommendation dataset, an unsupervised annotated dental product recommendation dataset, a real time dental product recommendation dataset, a real time dental product recommendation, a real time dental product recommendation for no product, an individual information dataset, a biometric dataset, a real time correlation dataset and provide to at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, an artificial intelligence system, a researcher, a manufacturer, a business, an application software, a system software, a processing device, a client device;

wherein a client device is at least one of: a server, a desktop computer, a workstation, a laptop computer, a cell phone, a tablet, a mobile device, a cloud based storage service.

16. The processing device of claim 12 , wherein the e-commerce organization includes a dental insurance service, and wherein the dental insurance service provides an insurance dataset including at least one of: an American dental association (ADA) code, a date, a claim identifier, a claim number, a duplicate claim associated with the claim identifier, a provider national identification number, a provider's state license number, a provider identification number;

wherein, an insurance dataset may be correlated to least one of: a dental image, a dental image landmark, an individual information dataset and may be provided to at least one of: a dental insurance service, a real time correlation dataset.

17. The processing device of claim 12 , wherein the e-commerce organization includes a bioinformatics service, and wherein the bioinformatics service provides a bioinformatics dataset including at least one of: a gene identifier, a gene sequence, a single nucleotide polymorphism, a nucleic acid sequence, a protein sequence, an annotating genome, a shotgun sequence, a periodontal disease, a caries susceptibility, an impacted tooth, a tooth loss, an angle's classification of malocclusion, a diabetes diagnosis;

wherein, a bioinformatics dataset may be correlated to least one of: a dental image, a dental image landmark, an individual information dataset and may be provided to at least one of: a bioinformatics service, a real time correlation dataset.

18. The following is claimed:

a method of generating a dental recommendation based on image processing, wherein a method comprising:

a processor configured to:

receive, using at least one of: a processing device, a communication device at least one patient data

from a dental insurance service comprising at least one image from at least one of: an expert device, a processing device, a patient device;

retrieve, using a storage device, at least one dental dataset;

analyze, using a processing device, at least one patient data and

at least one dental dataset, wherein at least one dental dataset is associated with at least one patient data, wherein at least one dental dataset comprises at least one of: a classified dental image

anatomy dataset, a classified dental image pathology dataset;

generate, using a processing device, at least one landmark based on analyzing, wherein at least one landmark comprises at least one dental characteristic associated with at least one patient data;

retrieve, using a storage device, at least one dental reference dataset;

process, using a processing device, at least one landmark and at least one dental reference dataset;

determine, using a processing device, at least one dental recommendation based on a processing;

transmit, using at least one of: a processing device, a communication device at least one dental recommendation to at least one external device;

transmit, using at least one of: a processing device, a communication device at least one dental recommendation and at least one patient data to at least one of: an expert device, a processing device, a patient device associated with at least one expert;

store, using a storage device, at least one dental recommendation;

receive, using at least one of: a processing device, a communication device a first confidence score from at least one of: an expert device, a processing device, a patient device, wherein a first confidence score is associated with at least one dental

recommendation;

receive, using at least one of: a processing device, a communication device at least one of: a second confidence score, a multiple confidence score from at least one of: an expert device, a processing device, a patient device;

wherein a second confidence score is associated with at least one landmark;

wherein a multiple confidence score is associated with at least one landmark;

transmit, using at least one of: a processing device, a communication device a first confidence score to at least one external device;

transmit, using at least one of: a processing device, a communication device at least one of: a second confidence score, a multiple confidence score to at least one external device;

update, using a processing device, at least one dental reference dataset with at least one patient data and at least one dental recommendation based on at least one of: a first confidence score, a second confidence score, a multiple confidence score;

generate, using a processing device, at least one updated reference dataset based on updating of at least one dental reference dataset;

store, using a storage device, at least one updated reference dataset;

receive an order from at least one patient device, wherein an order is associated with at least one dental recommendation;

transmit, using at least one of: a processing device, a communication device an order to at least one external device;

receive, using at least one of: a processing device, a communication device a response corresponding to an order from at least one external device;

transmit, using at least one of: a processing device, a communication device a response to at least one patient device;

generate, using a processing device, a validity notification corresponding to at least one patient

data based on an analyzing, wherein a validity notification is associated with a measure of approval of at least one patient data;

transmit, using at least one of: a processing device, a communication device a validity notification to at least one external device;

retrieve, using a storage device, at least one regulatory data based on at least one first user;

analyze, using a processing device, at least one first user data based on at least one regulatory data;

generate, using a processing device, a notification corresponding to at least one first user based on an analyzing of at least one first user data;

transmit, using at least one of: a processing device, a communication device a notification to at least one of: at least one patient device, at least one external device;

process a transaction from at least one of: a dental professional, a health care professional, an expert, an individual, an e-commerce organization, a researcher, a business, a government agency, a manufacturer, a business, an application software, a system software, a client device, a processing device of at least one of: an exchange, a transfer, a purchase, a sell of a dataset, wherein a dataset includes at least one of: one patient data, one dental dataset, one dental reference dataset, one landmark, one dental recommendation, a first confidence score, a second confidence score, a multiple confidence score, one dental product recommendation, a validity notification over at least one of: a processing device, a communication device, a communication network;

âwherein at least one of: a processing device, a communication device, a communication network includes at least one of: the internet, an intranet, an extranet, an internet, an internet transaction service, an online transaction service, a mobile network, a cell phone, a wearable technology, a wireless network, a cloud platform, an online transaction processing (OLTP) service, an online analytical processing (OLAP) service, a transaction platform and provide to at least one of: a dental professional, a health care professional, an expert, a processing device, a patient device, an individual, an e-commerce organization, a researcher, a business, a government agency, a manufacturer, a business, an application software, a system software, a client device, a processing device.

19. The method of claim 18 , wherein at least one dental reference dataset comprises at least one of: a dental treatment dataset, a dental image treatment dataset, a dental product dataset, and a dental image landmark dataset, wherein a processing device is configured for merging at least one of: a dental treatment data, a dental image treatment dataset, a dental product dataset, a dental image landmark dataset and provide to an artificial intelligence dataset, wherein an analyzing at least one of: a patient data, a dental dataset may be based on an artificial intelligence dataset.

20. The method of claim 18 , wherein a processing of at least one landmark and at least one dental reference dataset is based on at least one artificial intelligence model, wherein at least one artificial intelligence model is configured for determining of at least one dental recommendation, wherein at least one artificial intelligence model comprises at least one of: a deep neural network model, a machine learning model, a recurrent neural network model, a deep forest decision tree, an independent neural network model, an artificial intelligence platform.

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Cited By (8)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20210350530A1

( en )

2020-05-08

2021-11-11

Richard Ricci

Dental Images Correlated to the Human Genome with Artificial Intelligence

US20220202532A1

( en )

2019-04-24

2022-06-30

Dental Monioring

Method for evaluating an orthodontic aligner

US11419710B2

( en )

2015-07-07

2022-08-23

Align Technology, Inc.

Systems, apparatuses and methods for substance delivery from dental appliance

US11582200B2

( en )

2020-07-27

2023-02-14

Kpn Innovations, Llc.

Methods and systems of telemedicine diagnostics through remote sensing

US12109091B1

( en )

2018-11-07

2024-10-08

Align Technology, Inc.

Cryptographically secured dental treatment planning

WO2025032614A1

( en )

2023-08-09

2025-02-13

Healyantra Healthcare Innovations Private Limited

Digital dental handpiece with operatory field annotation and improved cooling mechanism

WO2025061103A1

( en )

2023-09-20

2025-03-27

The University Of Hong Kong

Data-based personalized site-specific care advising model powered with artificial-intelligence

US12347084B2

( en )

2021-09-22

2025-07-01

Optum, Inc.

Automated object transformation identification

Families Citing this family (6)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US11653838B2

( en )

2020-03-11

2023-05-23

Alireza Moheb

System and method for classification of dental health based on digital imagery

US12340510B2

( en )

2020-06-04

2025-06-24

The Procter & Gamble Company

Oral care based digital imaging systems and methods for analyzing attributes of a facial image portion

WO2023133297A2

( en )

2022-01-09

2023-07-13

Get-Grin Inc.

Collapsible dental scope

US20240079128A1

( en )

2022-09-01

2024-03-07

Shanghai United Imaging Intelligence Co., Ltd.

Systems and methods for providing digital healthcare services

CN116977905B

( en )

2023-09-22

2024-01-30

æå·ç±è¯åæºç§ææéå¬å¸

Target tracking method, device, electronic equipment and storage medium

US12333722B1

( en )

2024-03-28

2025-06-17

Enamel Pure, Inc.

Systems and methods for predicting medical conditions usingmachine learning correlating dental images and medical data

Citations (20)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20060184489A1

( en )

2004-12-17

2006-08-17

General Electric Company

Genetic knowledgebase creation for personalized analysis of medical conditions

US20090316966A1

( en )

2008-05-16

2009-12-24

Geodigm Corporation

Method and apparatus for combining 3D dental scans with other 3D data sets

US20110020785A1

( en )

2007-07-26

2011-01-27

T2 Biosystems, Inc.

Diagnostic Information Generation and Use

US20120051614A1

( en )

2009-05-05

2012-03-01

Koninklijke Philips Electronics N. V.

Automatic assessment of confidence in imaging data

US20130144790A1

( en )

2011-12-06

2013-06-06

Walter Clements

Data Automation

US20150294066A1

( en )

2014-04-09

2015-10-15

Douglas A. Golay

Combination dental imaging system and dental practice management and charting system with a bi-directional communication interface

US20160203599A1

( en )

2013-08-13

2016-07-14

H. Lee Moffitt Cancer Center And Research Institute, Inc.

Systems, methods and devices for analyzing quantitative information obtained from radiological images

US20160246781A1

( en )

2015-02-19

2016-08-25

Gary Cabot

Medical interaction systems and methods

US20170262614A1

( en )

2009-04-22

2017-09-14

Millennium Pharmacy Systems, Inc.

Pharmacy management and administration with bedside real-time medical event data collection

US20170329922A1

( en )

2015-03-06

2017-11-16

Azova, Inc.

Telemedicine platform with integrated e-commerce and third party interfaces

US20180028294A1

( en )

2016-07-27

2018-02-01

James R. Glidewell Dental Ceramics, Inc.

Dental cad automation using deep learning

US20180082030A1

( en )

2016-09-19

2018-03-22

International Business Machines Corporation

Automatic Adjustment of Treatment Recommendations Based on Economic Status of Patients

US20180168781A1

( en )

2016-12-16

2018-06-21

Align Technology, Inc.

Augmented reality enhancements for dental practitioners

US20180308569A1

( en )

2017-04-25

2018-10-25

S Eric Luellen

System or method for engaging patients, coordinating care, pharmacovigilance, analysis or maximizing safety or clinical outcomes

US20180322248A1

( en )

2017-05-02

2018-11-08

Coranet Solutions, Inc.

Mobile interoperable personal health information exchange with biometrics data analytics

US20180318709A1

( en )

2017-05-04

2018-11-08

Inspired Gaming (Uk) Limited

Generation of variations in computer graphics from intermediate formats of limited variability, including generation of different game appearances

US20190122770A1

( en )

2013-10-15

2019-04-25

Parkland Center For Clinical Innovation

Lightweight Clinical Pregnancy Preterm Birth Predictive System and Method

US20190214116A1

( en )

2018-01-10

2019-07-11

Cheryl Lee Eberting

Digital health platform for chronic disease management, secure messaging, prescription management, and integrated e-commerce curation

US20200005921A1

( en )

2016-09-12

2020-01-02

National Health Coalition, Inc.

Processing Pharmaceutical Prescriptions in Real Time Using a Clinical Analytical Message Data File

US20200100724A1

( en )

2014-08-11

2020-04-02

Douglas A. Golay

Applying non-real time and non-user attended algorithms to stored non-imaging data and existing imaging data for obtaining a dental diagnosis

Family Cites Families (2)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US8866883B2

( en )

2007-06-29

2014-10-21

3M Innovative Properties Company

Synchronized views of video data and three-dimensional model data

WO2019204520A1

( en )

2018-04-17

2019-10-24

VideaHealth, Inc.

Dental image feature detection

2020

2020-05-08

US16/870,749

patent/US10937160B1/en

active

Active

2020-12-30

US17/138,641

patent/US11948685B2/en

active

Active

Patent Citations (20)

* Cited by examiner, â Cited by third party

Publication number

Priority date

Publication date

Assignee

Title

US20060184489A1

( en )

2004-12-17

2006-08-17

General Electric Company

Genetic knowledgebase creation for personalized analysis of medical conditions

US20110020785A1

( en )

2007-07-26

2011-01-27

T2 Biosystems, Inc.

Diagnostic Information Generation and Use

US20090316966A1

( en )

2008-05-16

2009-12-24

Geodigm Corporation

Method and apparatus for combining 3D dental scans with other 3D data sets

US20170262614A1

( en )

2009-04-22

2017-09-14

Millennium Pharmacy Systems, Inc.

Pharmacy management and administration with bedside real-time medical event data collection

US20120051614A1

( en )

2009-05-05

2012-03-01

Koninklijke Philips Electronics N. V.

Automatic assessment of confidence in imaging data

US20130144790A1

( en )

2011-12-06

2013-06-06

Walter Clements

Dental images processed with artificial intelligence — Richard Ricci (US10937160B1)

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