Abstract: Embodiments for generating patient-specific implants for anatomical structures of patients are disclosed. One embodiment includes a method that includes receiving image data of an anatomical structure of a patient, where the image data indicates a fracture site associated with the anatomical structure; receiving manipulation data associated with manipulation of one or more parameters associated with the fracture site in a virtual reality environment; visualizing a three-dimensional (3D) model of the anatomical structure based on the image data, the one or more parameters, and the manipulation data, where the 3D model is visualized in a 3D space; generating a set of instructions for a printer based on the 3D model, and controlling the printer for generating a 3D printed model of the anatomical structure based on the set of instructions. The 3D printed model is configured to receive an implant, such that the implant is contoured to match the fracture site.

Abstract: The present technology relates to devices and systems for multidimensional data visualization and interaction in an augmented reality, virtual reality, or mixed reality image guided surgery. The disclosed embodiment provides a tool for a physician or other medical specialist to load and review medical scans in an AR/VR/MR environment, assisting medical diagnostics, surgical planning, medical education, or patient engagement.

Abstract: Embodiments of the present disclosure provide a system, a method, and a computer programmable product for visualizing volume layers on an object accurately. The system retrieves imaging data relating to an object, generates a set of volume layers corresponding to the object based on the imaging data, and generates a set of guide attributes associated with a virtual guide for the object based on the imaging data. The virtual guide includes an object-specific structure. The system causes to project the virtual guide at least in association with the object and visualizes the set of volume layers in association with the object based on the projected virtual guide.

Abstract: The present technology relates to devices and systems for multidimensional data visualization and interaction in an augmented reality, virtual reality, or mixed reality image guided surgery. The disclosed embodiment provides a tool for a physician or other medical specialist to load and review medical scans in an AR/VR/MR environment, assisting medical diagnostics, surgical planning, medical education, or patient engagement.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.

Abstract: A “Facet Recommender” creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks.