Abstract: A method for treatment-based photo guidance may include receiving a treatment plan, determining the movement of a first one or more teeth based on the movement of tooth models in the treatment plan, and determining one or more photo views for capturing the movement of the first one or more teeth.

Abstract: A method for dental treatment may include receiving photos of a patient's dentition, gathering treatment parameters for at treatment plan. The treatment parameters may represent attributes of the treatment plan selected by a doctor for the patient's dentition of a patient. The method may include generating, using the photos and the treatment parameters, one or more intelligent guidance rules to guide application of at least a portion of the treatment parameters to the patient's dentition, generating instructions to apply the intelligent patient guidance rules to the patient's dentition, and providing the instructions to the patient to apply the at least a portion of the treatment plan in accordance with the intelligent patient guidance rules.

Abstract: A method for image-based orthodontic treatment may include receiving image data of a patient's dentition and an orthodontic appliance, identifying, from the image data, the orthodontic appliance, calculating a misalignment height of a misalignment of the orthodontic appliance with respect to the patient's dentition, determining whether the misalignment height satisfies a misalignment threshold, and in response to satisfying the misalignment threshold, providing a notification.

Abstract: Systems and methods are directed to a pull-through winding method, and a manufacturing fixture, or mandrel, that guides the layup of material in the pull-through winding method, for fabrication of a three-dimensional load bearing structure. These systems and methods allow fibers of material of the 3D load bearing structure to be pulled as straight as possible during winding, and maintained as straight as possible during curing, to take full advantage of properties associated with the fiber or filament-based materials used in these structures, including, for example, carbon fiber material. The manufacturing fixture includes a plurality of pull-through slots at end portions thereof, that provide for the pulling of the fiber-based material, and the application and maintenance of tension in the material that maintains the alignment of the fibers as the material is wound, cured and hardened.

Abstract: A raised foundation system for supporting a prefabricated real property structure having a downwardly facing mounting structure. The system includes a slab having footers; for each footer, a column attached thereto; a plurality of elongated main braces attached to the columns; and a plurality of elongated lateral braces attached to the main braces. At least two of the main braces extend along a plane, and each lateral brace extends the plane or another plane parallel to the plane. The main and lateral braces form an upwardly facing mounting structure configured to attach to the downwardly facing mounting structure.

Abstract: Various apparatuses are disclosed (e.g., system, device, method, or the like) for guiding the capture and assessing the quality of an image, including dental images. The apparatuses may use trained neural networks to examine images and provide users feedback regarding image quality. The neural networks may be trained based on images within image groups that have been ranked based on perceived image quality.

Abstract: Provided herein are systems and methods for scoring a post-treatment tooth position of a patient's teeth. A patient's dentition may be scanned and/or segmented. Raw dental features, principal component analysis (PCA) features, and/or other features may be extracted and compared to those of other teeth, such as those obtained through automated machine learning systems. A classifier can identify and/or output the post-treatment tooth position of the patient's teeth.

Abstract: A method of dental treatment may include receiving photos of a person's dentition, identifying a stage of a treatment plan administered to the person's dentition, gathering a three-dimensional (3D) model of the person's dentition corresponding to the stage of the treatment plan, projecting attributes of the 3D model of the person's dentition onto an image plane to get a projected representation of the person's dentition at the stage of the treatment plan, comparing the photos to the projected representation to derive an error image representing the comparison, and analyzing the error image for discrepancies, wherein the discrepancies represent one or more deviations of the person's dentition from the stage of the treatment plan.

Abstract: In a technique to assess the blurriness of an image, an image of a face is received, the image including a depiction of lips. A processing device determines a region of interest in the image, wherein the region of interest comprises an area inside of the lips. The processing device applies a focus operator to the pixels within the region of interest, and calculates a sharpness metric for the region of interest using an output of the focus operator. The processing device determines whether the sharpness metric satisfies a sharpness criterion, and one or more additional operations are performed responsive to determining that the sharpness metric satisfies the sharpness criterion.

Abstract: A cloud-based system for use by retail store employees or customers at any location to facilitate the sale of automotive tires to consumers is provided. The system accesses multiple independent tire inventory systems from different distributors/manufacturers and provides a personalized set of recommendation tire options and accompanying TPMS service packs.

Abstract: This disclosure relates to methods for identifying molecular arrangements useful in managing diseases or conditions. In certain embodiments, this disclosure relates to treating an immune regulated disease comprising administering to a subject in need thereof an effective amount of a grouped molecular arrangement comprising a specific binding agent to CD19, a specific binding agent to CD3, and IL-12. In certain embodiments, this disclosure relates to screening test compounds comprising providing a library of multifunctional binding specificities and contacting the library with cells to evaluate in vitro therapeutic potential.

Abstract: A method for utilizing shared memory spaces for data lifecycle management in a conversational AI system includes receiving one or more storage requests directing conversational data to multiple nodes of a conversational data object and storing the conversational data that is directed to each different one of the multiple nodes within a different one of multiple discrete memory spaces. Each of the multiple discrete memory spaces is governed by a scope policy defining a lifetime for data stored within the memory space. The method further provides for erasing data of each node of the multiple different nodes of the conversational data object according to the lifetime defined by the scope policy governing the memory space storing the node.

Abstract: Technologies are described herein for denormalizing data instances. Schemas for data instances are embedded with annotations indicating how the denormalization is to be performed. Based on the annotations, one or more sub per object indexes (“sub POIs”) can be generated for each data instance and stored. The sub POIs can include a target sub POI containing data from the data instance, and at least one source sub POI containing data from another data instance, if the data instance depends on the other data instance. Data instance updates can be performed by identifying sub POIs that are related to the updated data instance in storage, and updating the related sub POIs according to the update to the data instance. The sub POIs can be sent to an indexing engine to generate an index for a search engine to facilitate searches on the data instances.

Abstract: Techniques are provided for building a dialog-state specific contextual language understanding system using subsumption logic. Information establishing conversational rules identifying the conversational dialog is received to present in respective dialog states. Each rule has a Boolean trigger expression of predicates for testing the conversational state together with logical connectives to identify when the rule is applicable. Subsumption logic is used to arrange the rules into a directed acyclic graph (DAG) where more specific rules are preferred to more general rules. During a conversation, the DAG is used to filter the triggered rules to only the most specific triggered rules from which a rule to run is selected. This structure makes it easier to build conversational systems because rules can be added or removed without having to change or reason over other rules. The rules also act as a constraint to help machine learned selection systems converge with less data.

Abstract: A computer-executed conversational system utilizing adaptive dialogs to provide dynamic modification of a conversation structure. The adaptive dialogs facilitate an adaptive and dynamic way to model conversations. A conversational rule may be associated with a trigger received at an adaptive dialog. The conversational rule may populate a plan with steps to provide the conversational structure of the conversational system. Modifying triggers may be received that allow conversational rules to amend the plan including adding steps, removing steps, or modifying steps from the plan. The adaptive dialogs and/or components thereof may be provided as constructible elements in a platform for building a bot such as a developer Software Development Kit (SDK) or the like. In turn, sophisticated conversational modeling may be conveniently facilitated to provide features such as a dialog dispatcher, the ability to handle interruptions, and to build a pluggable, extensible conversational system.

Abstract: Conversational event modeling for determining how to process an input in a dialog stack of a computer-executed conversational system. Receipt of an input at an active dialog in a dialog stack may result in generation of an event corresponding to the input. The event may be propagated through the dialog stack. Dialogs in the dialog stack may generate a confidence value in relation to processing the input, and selection of the dialog in the dialog stack for use in processing the input may be based at least in part on the confidence values. In turn, the conversational system may facilitate improved handling of ambiguous or unrelated inputs at dialogs by propagating the event associated with such an input through the dialog stack. The event creation and processing functions may be provided as parameters for dialogs in a modular dialog creation system.

Abstract: Conversational event modeling for determining how to process an input in a dialog stack of a computer-executed conversational system. Receipt of an input at an active dialog in a dialog stack may result in generation of an event corresponding to the input. The event may be propagated through the dialog stack. Dialogs in the dialog stack may generate a confidence value in relation to processing the input, and selection of the dialog in the dialog stack for use in processing the input may be based at least in part on the confidence values. In turn, the conversational system may facilitate improved handling of ambiguous or unrelated inputs at dialogs by propagating the event associated with such an input through the dialog stack. The event creation and processing functions may be provided as parameters for dialogs in a modular dialog creation system.

Abstract: A method for utilizing shared memory spaces for data lifecycle management in a conversational AI system includes receiving one or more storage requests directing conversational data to multiple nodes of a conversational data object and storing the conversational data that is directed to each different one of the multiple nodes within a different one of multiple discrete memory spaces. Each of the multiple discrete memory spaces is governed by a scope policy defining a lifetime for data stored within the memory space. The method further provides for erasing data of each node of the multiple different nodes of the conversational data object according to the lifetime defined by the scope policy governing the memory space storing the node.

Abstract: A computer-executed conversational system utilizing adaptive dialogs to provide dynamic modification of a conversation structure. The adaptive dialogs facilitate an adaptive and dynamic way to model conversations. A conversational rule may be associated with a trigger received at an adaptive dialog. The conversational rule may populate a plan with steps to provide the conversational structure of the conversational system. Modifying triggers may be received that allow conversational rules to amend the plan including adding steps, removing steps, or modifying steps from the plan. The adaptive dialogs and/or components thereof may be provided as constructible elements in a platform for building a bot such as a developer Software Development Kit (SDK) or the like. In turn, sophisticated conversational modeling may be conveniently facilitated to provide features such as a dialog dispatcher, the ability to handle interruptions, and to build a pluggable, extensible conversational system.

Abstract: Techniques are provided for building a dialog-state specific contextual language understanding system using subsumption logic. Information establishing conversational rules identifying the conversational dialog is received to present in respective dialog states. Each rule has a Boolean trigger expression of predicates for testing the conversational state together with logical connectives to identify when the rule is applicable. Subsumption logic is used to arrange the rules into a directed acyclic graph (DAG) where more specific rules are preferred to more general rules. During a conversation, the DAG is used to filter the triggered rules to only the most specific triggered rules from which a rule to run is selected. This structure makes it easier to build conversational systems because rules can be added or removed without having to change or reason over other rules. The rules also act as a constraint to help machine learned selection systems converge with less data.