Abstract: In one embodiment, an AR/VR system includes a social-networking application installed on the AR/VR system, which allows a user to access on online social network, including communicating with the user's social connections and interacting with content objects on the online social network. The AR/VR system also includes an AR/VR application, which allows the user to interact with an AR/VR platform by providing user input to the AR/VR application via various modalities. Based on the user input, the AR/VR platform generates responses and sends the generated responses to the AR/VR application, which then presents the responses to the user at the AR/VR system via various modalities.

Abstract: Described is a multiple-camera system and process for re-identifying a user located in a materials handling facility based on user patterns and/or descriptors representative of the user. In one implementation, a user pattern and/or a plurality of descriptors representative of a user are maintained as a position of a user is tracked through a materials handling facility. If the tracking of the user is lost, the last known position is stored with the user pattern and/or descriptors. If a new object is detected and confirmed to be a user, a user pattern and/or descriptors of the new object are compared with the stored user pattern and/or descriptors to determine if the new object is the user.

Abstract: In one embodiment, a system includes an automatic speech recognition (ASR) module, a natural-language understanding (NLU) module, a dialog manager, one or more agents, an arbitrator, a delivery system, one or more processors, and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to receive a user input, process the user input using the ASR module, the NLU module, the dialog manager, one or more of the agents, the arbitrator, and the delivery system, and provide a response to the user input.

Abstract: Markers may be presented on different objects in a facility, such as shelves or totes. A marker can use color, placement of elements, blending effects, or infrared light layers to encode data about the object. During operation, a camera within the facility may acquire an image, which includes an image of the marker as presented on the object. The marker can be analyzed to detect marker data encoded therein. By decoding the marker data, details about the marker can be extracted, such as an identification of the object. In addition, using details about the marker location within the image and location data associated with the camera, a location of the object relative to the camera can be determined, as well as an orientation of the object relative to the camera. As the object moves within the facility, the object can be tracked based on this location and orientation data.

Abstract: Methods and apparatuses are described for building a knowledge base taxonomy from structured or unstructured computer text for use in automated user interactions. A server computing device receives one or more of structured text or unstructured text corresponding to historical user interaction data from a database. The server computing device extracts one or more terms from the received text that are most relevant to a subject matter domain. The server computing device organizes the extracted one or more terms into a taxonomy data structure.

Abstract: Described is a multiple-camera system and process for detecting a user within a materials handling facility and tracking a position of the user as the user moves through the materials handling facility. In one implementation, a plurality of depth sensing cameras are positioned above a surface of the materials handling facility and oriented to obtain a top-down view of the surface of the materials handling facility, along with any objects (e.g., users) on the surface of the materials handling facility. The depth information from the cameras may be utilized to detect objects on the surface of the materials handling facility, track a movement of those objects and determine if those objects are users.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: Methods and apparatuses are described for building a knowledge base taxonomy from structured or unstructured computer text for use in automated user interactions. A server computing device receives one or more of structured text or unstructured text corresponding to historical user interaction data from a database. The server computing device extracts one or more terms from the received text that are most relevant to a subject matter domain. The server computing device organizes the extracted one or more terms into a taxonomy data structure.

Abstract: Machine-readable optical markers may be presented on a surface of a shelf, on a display device, or on a structure. Markers are presented as an array of graphical elements arranged in rows and columns. The graphical elements in the array of the mark utilize color and placement to encode marker data. In addition, a source image graphic may also be blended with a portion of the graphical elements in the array to create a blended image. In other embodiments, the marker may be presented with one or more borders surrounding the perimeter of the graphical elements in the array. The marker data may be presented using infrared light with a visible light layer presented over the array of graphical elements.

Abstract: Described is a multiple-camera system and process for re-identifying a user located in a materials handling facility based on user patterns and/or descriptors representative of the user. In one implementation, a user pattern and/or a plurality of descriptors representative of a user are maintained as a position of a user is tracked through a materials handling facility. If the tracking of the user is lost, the last known position is stored with the user pattern and/or descriptors. If a new object is detected and confirmed to be a user, a user pattern and/or descriptors of the new object are compared with the stored user pattern and/or descriptors to determine if the new object is the user.

Abstract: A computerized method of authenticating a user includes receiving, by a computing device, first biometric data from the user via a first biometric input device; extracting one or more first features from the first biometric data; generating a first vector based on the first features; computing a first similarity score reflecting a geometric distance between the first vector and a first reference vector stored in memory; receiving second biometric data from the user via a second biometric input device; extracting one or more second features from the second biometric data; generating a second vector based on the second features; computing a second similarity score reflecting a geometric distance between the second vector and a second reference vector stored in memory; computing a confidence score based on the first and second similarity scores; and determining a right of the user to access restricted information based on the confidence score.

Abstract: Described is a multiple-camera system and process for re-identifying a user located in a materials handling facility based on user patterns and/or descriptors representative of the user. In one implementation, a user pattern and/or a plurality of descriptors representative of a user are maintained as a position of a user is tracked through a materials handling facility. If the tracking of the user is lost, the last known position is stored with the user pattern and/or descriptors. If a new object is detected and confirmed to be a user, a user pattern and/or descriptors of the new object are compared with the stored user pattern and/or descriptors to determine if the new object is the user.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: Described are systems and techniques for using machine-readable optical markers to calibrate a machine vision system. In one implementation, the machine vision system may generate calibration data associating spatial coordinates with image coordinates of the machine-readable marker in an image acquired by a camera. The machine-readable marker may comprise a blend of an optical code that encodes marker data and a human-readable source graphic image. In some implementations, the optical code of the markers may be detectable under infrared light while remaining invisible to the unaided eye.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: Examples for detecting and removing non-natural language within natural language to enhance performing content analysis on the natural language are provided herein. A plurality of terms is identified in a phrase, and a sliding window having a defined length is placed over a first sequence of terms from the plurality of terms. The first sequence of terms includes a first term, a second term, and a third term, the first term and the third term being adjacent to the second term. Based on the first term, the second term, and the third term, a determination is made as to whether the second term represents non-natural language. Upon determining that the second term is non-natural language, the second term is labeled as non-natural language and is removed from the plurality of terms based on determining the second term as non-natural language.

Abstract: Systems and methods acquire and/or generate multiple different images of the same biometric identity, identify specific instances of biometric features in each of the different images, and merge the identified specific instances of biometric features into a data record that provides a digital representation of the biometric identity. Examples of biometric identities include fingerprints, handprints, palm prints, and thumbprints. In one embodiment, a counter is associated with each specific instance of a biometric feature found in the multiple images. Specific instances of biometric features found most frequently have high counts and are indicative of true identifications; those with low counts are indicative of false identifications. A threshold distinguishes between true and false identifications. Those specific instances with counts below the threshold are excluded when the digital representation of the biometric identity is generated.

Abstract: A video hosting web site receives uploaded video content and processes the video to determine ad surfaces. The ad surfaces comprise spatio-temporal regions of the video suitable for placement of advertisement such as background surfaces or other regions of low interest. The uploaded video and ad surfaces are stored to a video database that is accessible to viewers visiting the video hosting web site. When a shared video is requested, the video hosting site provides the requested video content together with the ad surfaces and advertisements. The advertisements are blended with the ad surfaces in the video at playtime so that the advertisements appear as part of the video scene. This allows the video hosting web site to present advertisements to the viewer without significantly distracting the viewer from the requested content.

Abstract: A video hosting web site receives uploaded video content and processes the video to determine ad surfaces. The ad surfaces comprise spatio-temporal regions of the video suitable for placement of advertisement such as background surfaces or other regions of low interest. The uploaded video and ad surfaces are stored to a video database that is accessible to viewers visiting the video hosting web site. When a shared video is requested, the video hosting site provides the requested video content together with the ad surfaces and advertisements. The advertisements are blended with the ad surfaces in the video at playtime so that the advertisements appear as part of the video scene. This allows the video hosting web site to present advertisements to the viewer without significantly distracting the viewer from the requested content.