Abstract: Systems and methods are provided for automatically marking locations within a radiograph of one or more dental pathologies, anatomies, anomalies or other conditions determined by automated image analysis of the radiograph by a number of different machine learning models. Image annotation data may be generated based at least in part on obtained results associated with output of the multiple machine learning models, where the image annotation data indicates at least one location in the radiograph and an associated dental pathology, restoration, anatomy or anomaly detected at the at least one location by at least one of the machine learning models. A number of different pathologies may be identified and their locations marked within a single radiograph image.

Abstract: Systems and methods are described for automatically evaluating a claim submitted to an insurance carrier. Claim information and at least one image associated with the claim may be received, where the image has been submitted to a carrier as supporting evidence of a service performed by a submitter of the claim. The system may provide image data and other claim information from the submitted claim as input to machine learning models configured to identify whether the image data, such as a radiograph, supports the other data in the claim submission, such as a treatment code.

Abstract: Systems and methods for enhanced techniques for determination of dental margins in intraoral scans. An example method includes receiving a request including an intraoral scan of a portion of a mouth, the intraoral scan depicting a margin created via adjustment of a tooth. A prepared region depicting the tooth is identified based on the intraoral scan. A representation of the intraoral scan usable as input to a machine learning model is generated, with the representation comprising structured data associated with the point cloud or mesh. Information identifying an estimated margin is determined based on the representation via computing a forward pass of the machine learning model. A response comprising the determined information is generated, with the determined information being usable to fabricate a prosthetic according to the estimated margin.

Abstract: Water-based polyurethane dispersion methodology relating to a chain-extended polyurethane, characterized in that the polyurethane dispersion includes 2, 5, 7, 10-tetraoxaundecane. The water-based polyurethane dispersion is suitable as a non-hazardous water-based coating composition for coatings with a particularly good adhesion on plastic or rubber substrates. The solvent 2, 5, 7, 10-tetraoxaundecane contained therein imparts good film coalescing, good adhesive properties and high strength of the polyurethane dispersion.

Abstract: Systems and methods are provided for analyzing images or video using computer vision. Data comprising real time or near real time information or historical information is retrieved that is associated with a sporting event at a physical location. A time segment is identified of a display device at the physical location for acquisition. The display device is configurable to present visual sponsorship data during the time segment for an assigned sponsor. It is determined that one or more rules are satisfied by the data. An indication is transmitted that the first rule is satisfied to a computing device of a sponsor. A bid or valuation is generated based at least on the first rule being satisfied. A request to acquire the time segment is received from the computing device of the sponsor, and the display device at the physical location is caused to present visual sponsorship data for the sponsor during the time segment.

Abstract: Systems and methods are described for training machine learning models to detect objects in image or video data. A system may select a first sample set of frames from one or more video files. Indications of a location of an object of interest in each of at least two sample frames may be received, then the system may determine the location of the object of interest across a number of intermediary frames using a tracker. Annotation data may be stored identifying the objects of interest in the sample frames, and the annotation data may be used in training a machine learning model to identify the object of interest in subsequently provided image or video data.

Abstract: Systems and methods are described for identifying at least one sports team depicted in media content, such as image or video data. Features of the media content may be provided as input to a first set of classification models that are each trained to identify at least one type of scene associated with one or more sports, then features of the media content may be provided to a second set of classification models trained to identify at least one object associated with one or more sports. Once a sport depicted in the media content is determined based on the first and second set of classification models, the system may determine a team depicted in the media content based at least in part by comparing aspects of the media content to stored data associated with various teams that play the identified sport.

Abstract: Systems and methods are provided for identifying one or more portions of images or video frames that are appropriate for augmented overlay of advertisement or other visual content, and augmenting the image or video data to include such additional visual content. Identifying the portions appropriate for overlay or augmentation may include employing one or more machine learning models configured to identify objects or regions of an image or video frame that meet criteria for visual augmentation. The pose of the augmented content presented within the image or video frame may correspond to the pose of one or more real-world objects in the real world scene captured within the original image or video.

Abstract: Systems and methods are provided for analyzing images or video using computer vision. Media items, including images or videos, are retrieved from one or more media channels, such as social media network services, streaming media networks, or broadcast networks. A media item is identified that depicts a object associated with a sponsor. A media cost equivalent is determined that is associated with the media item as distributed by the media channels. Using computer vision, a percentage is determined for the media cost equivalent. The percentage is determined based at least in part on at least one of size, clarity, duration or position of the sponsor object or another object in the media item. A sponsor valuation is calculated based at least in part on the media cost equivalent adjusted by the percentage.

Abstract: Systems and methods are described for augmenting video data based on automated identification of one or more objects depicted in the video data. One or more classification models may identify an object of interest in video data. An aggregated duration count may be maintained that reflects a length of time that the object of interest has been depicted in the video data. This duration or additional metric data derived in part from the duration may be displayed in association with display of the video data and continuously updated during playback of the video data.

Abstract: A water-based polyurethane dispersion including a chain-extended polyurethane, characterized in that the polyurethane dispersion includes 2,5,7,10-tetraoxaundecane. The water-based polyurethane dispersion is suitable as a non-hazardous water-based coating composition for coatings with a particularly good adhesion on plastic or rubber substrates. The solvent 2,5,7,10-tetraoxaundecane contained therein imparts good film coalescing, good adhesive properties and high strength of the polyurethane dispersion.

Abstract: Systems and methods are described for using video fingerprinting to detect depiction of one or more objects of interest in video data. An object of interest may first be identified in one or more frames of video data. A system may then create a digital video fingerprint representing the one or more frames in which the object is depicted. A potentially large amount of subsequent video data may then be received or retrieved, and the system may determine that the object appears in a portion of the subsequent video data based at least in part by identifying that the digital video fingerprint at least substantially matches a portion of the subsequent video data.

Abstract: In particular embodiments, a method for transmitting authentication data using acoustical means, comprising: encoding, by a first device, information into an acoustic signal; emitting, by an audio output component of the first device, the acoustic signal; detecting, by an audio input component of a second device, the acoustic signal; and decoding, by the second device, the acoustic signal into one or more pieces of information.

Abstract: Systems and methods are described for augmenting video data based on automated identification of one or more objects depicted in the video data. One or more classification models may identify an object of interest in video data. An aggregated duration count may be maintained that reflects a length of time that the object of interest has been depicted in the video data. This duration or additional metric data derived in part from the duration may be displayed in association with display of the video data and continuously updated during playback of the video data.

Abstract: Systems and methods are described for using video fingerprinting to detect depiction of one or more objects of interest in video data. An object of interest may first be identified in one or more frames of video data. A system may then create a digital video fingerprint representing the one or more frames in which the object is depicted. A potentially large amount of subsequent video data may then be received or retrieved, and the system may determine that the object appears in a portion of the subsequent video data based at least in part by identifying that the digital video fingerprint at least substantially matches a portion of the subsequent video data.

Abstract: Systems and methods are described for training machine learning models to detect objects in image or video data. A system may select a first sample set of frames from one or more video files. Indications of a location of an object of interest in each of at least two sample frames may be received, then the system may determine the location of the object of interest across a number of intermediary frames using a tracker. Annotation data may be stored identifying the objects of interest in the sample frames, and the annotation data may be used in training a machine learning model to identify the object of interest in subsequently provided image or video data.

Abstract: Systems and methods are provided for analyzing images or video using computer vision. Data comprising real time or near real time information or historical information is retrieved that is associated with a sporting event at a physical location. A time segment is identified of a display device at the physical location for acquisition. The display device is configurable to present visual sponsorship data during the time segment for an assigned sponsor. It is determined that one or more rules are satisfied by the data. An indication is transmitted that the first rule is satisfied to a computing device of a sponsor. A bid or valuation is generated based at least on the first rule being satisfied. A request to acquire the time segment is received from the computing device of the sponsor, and the display device at the physical location is caused to present visual sponsorship data for the sponsor during the time segment.

Abstract: Systems and methods are provided for analyzing images or video using computer vision. Media items, including images or videos, are retrieved from one or more media channels, such as social media network services, streaming media networks, or broadcast networks. A media item is identified that depicts a object associated with a sponsor. A media cost equivalent is determined that is associated with the media item as distributed by the media channels. Using computer vision, a percentage is determined for the media cost equivalent. The percentage is determined based at least in part on at least one of size, clarity, duration or position of the sponsor object or another object in the media item. A sponsor valuation is calculated based at least in part on the media cost equivalent adjusted by the percentage.

Abstract: Systems and methods are described for identifying at least one sports team depicted in media content, such as image or video data. Features of the media content may be provided as input to a first set of classification models that are each trained to identify at least one type of scene associated with one or more sports, then features of the media content may be provided to a second set of classification models trained to identify at least one object associated with one or more sports. Once a sport depicted in the media content is determined based on the first and second set of classification models, the system may determine a team depicted in the media content based at least in part by comparing aspects of the media content to stored data associated with various teams that play the identified sport.

Abstract: The present application is directed at the reaction product of an azide compound having two or more azide groups attached thereto and an alkyne compound having two or more alkyne groups attached thereto, wherein the azide and alkyne groups react in a 1,3-dipolar cyclo addition to form 1,4-disubstituted triazols and wherein the azide or alkyne compound or both include —O—(C?O)—NR— functional groups. The reaction products can be used as coatings, such as for flat roofs, sealants, adhesives and in elastomer applications. Methods for producing the reaction products as well as substrates including a coating of the reaction product are also disclosed.