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.

Abstract: A digital personal assistant is described that determines a mental or emotional state of a user based on one or more signals and, based on the determined mental or emotional state, provides the user with feedback concerning an item of content generated thereby or an activity to be conducted thereby. An API is described that can be used by diverse applications and/or services to communicate with the digital personal assistant for the purpose of obtaining information about the current mental or emotional state of the user. Content tagging logic is described that identifies one or more items of content generated or interacted with by the user and stores metadata in association with the identified item(s) of content. The metadata includes information indicative of the current mental or emotional state of the user during the time period when the user generated or interacted with the content.

Abstract: A spinal implant provides support for desired parts of the spine. The implant can provide support in both fusion and non-fusion situations. The spinal implant includes an implant rod and fasteners for coupling or fastening the implant rod to the affected regions of the spine via the pedicles of the affected vertebrae). The implant rod includes a flexible portion and rigid end portions. The fasteners couple the end portions of the rod to the pedicles in the affected level. The flexible portion can take a variety of shapes, such as non-helical, multi-curve springs. One may combine several implant rods to provide an overall implant for more than one level of the spine. The implant can allow desired motion while tending to limit undesirable motion, thus protecting areas of the diseased or injured spine, such as the nucleus pulposus or anulus fibrosis. Furthermore, the implant can provide a combination of rigid and flexible support, as desired.

Abstract: A spinal implant is provided which maintains intervertebral spacing and stability within the spine. The spinal implant may include a body and an insert. The body of the spinal implant may be formed of a ceramic material. In some embodiments, the body may be formed of beta tricalcium phosphate. The body may include an opening that is complementary to the insert. The insert may fit within the opening. The insert may include a number of passageways. Some of the passageways may intersect to form a scaffold for bone growth. Bone growth promoting material may be introduced into the insert before the insert is positioned in a body and inserted in a patient between two vertebrae.

Abstract: A spinal implant may be used to stabilize a portion of a spine. The implant may promote bone growth between adjacent vertebrae that fuses the vertebrae together. An implant may include an opening through a height of a body of the implant. The body of the implant may include curved sides. A top and/or a bottom of the implant may include protrusions that contact and/or engage vertebral surfaces to prevent backout of the implant from the disc space. A variety of instruments may be used to prepare a disc space and insert an implant. The instruments may include, but are not limited to, a distractor, a rasp, and one or more guides. The implant and instruments may be supplied in an instrument kit.