Abstract: A multi-arm robotic surgical system having an endoscopic camera (C) coupled to a robotic arm and a plurality of surgical instruments each detachably coupled to a robotic arm. The multi-arm robotic surgical system further comprises of a surgeon console having a master controller, a left-hand controller and a right-hand controller, a three-dimensional (3D) HD monitor, a two-dimensional (2D) touch screen monitor, a pair of trackable 3D glasses to be worn by the surgeon, a head tracking camera, a foot pedal controller, a control panel for performing emergency system functions, an electrosurgical unit interface, an image processor configured to process a 3D image data received from the endoscopic camera (C), a digital device configured to prepare an image data by using the processed 3D image data received from the image processor and superimposing it with a 2D overlay including a status of the plurality of surgical instruments and display on the 3D HD monitor and a patient side staff 3D monitor.

Abstract: The application provides an articulating cannula (300) comprising a proximal end (302) and a distal end (304) connected with a hollow tubular shaft (306). The distal end (304) is configured to articulate between a first position (310) and a second position (312). The shaft (306) is segmented into a first shaft (414) and a second shaft (316) which are secured by a flexible material. The proximal end (302) comprises of a housing (308) having diameter substantially greater than the diameter of the shaft (306) and configured to allow insertion of an articulating surgical instrument (318). The second shaft (316) comprises of an outer layer (320) and an inner layer (322) of the flexible material with a compression spring (324) in between, the second shaft (316) is configured to facilitate the articulation of the distal end (304) and to prevent collapse of second shaft (316) at the distal end (304).

Abstract: An active sparse labeling system that provides high performance and low annotation costs by performing partial instance annotation (i.e., sparse labeling) by frame level selection to annotate the most informative frames, thereby improving action detection task efficiencies. The active sparse labeling system utilizes a frame level cost estimation to determine the utility of each frame in a video based on the frame's impact on action detection. The system includes an adaptive proximity-aware uncertainty model, which is an uncertainty-based frame scoring mechanism. The adaptive proximity-aware uncertainty model estimates a frame's utility using the uncertainty of detections of the frame's proximity to existing annotations, thereby determining a diverse set of frames in a video which are effective for learning the task of dense video understanding (such as action detection).

Abstract: A hand controller assembly (200) of a console system (105) is disclosed herein. The hand controller assembly may include a backend assembly (209), where the backend assembly (209) or (301) is configured to facilitate movement of the hand controller (207b) in x, y, and z-coordinate axes. The hand controller assembly (200) further includes a left-hand controller (201a) and a right-hand controller (201b). The left and-controller (201a) includes a plurality of link members (211a) coupled to one another to form a linkage and the right-hand controller (201b) includes a plurality of link members (211b) coupled to one another to form a linkage. Proximal end of the plurality of link members (211a), (211b) is operationally secured to the backend assembly (209) and distal of the plurality of link member (211a), (211b) is operationally secured to a gimbal assembly (213a), (213b) respectively.

Abstract: A system and method for switching one or more User Equipment (UEs) 116A-N from a unicast mode to a broadcast or multicast mode to transmit a streaming media content to the UEs 116A-N is provided. The system includes, an Over-the-top (OTT) platform 104, a CDN 112, the UEs 116A-N, a cellular core network 202, one-to-many offload core 204, an analytics engine 206, a database 208, one-to-many transmitter 210, a real time switching module 212, a Cellular base station 214 and a user specified rules module 222. The analytics engine 206 continuously analyzes real-time and historical data stored in the database 208 to identify the UEs 116A-N that receive a streaming media content through the unicast mode and the streaming media content to be offloaded. An offload is a process by which certain portions of the streaming media content is shifted from the unicast mode to the broadcast or multicast mode.

Abstract: Methods of detecting and categorizing an action in an untrimmed video segment regardless of the scale of the action and the close proximity of other actions. The methods improve upon the prior art which either require trimmed video segments including only a single activity depicted therein, or untrimmed video segments including relatively few actions, persons, or objects of interest, thereby directing the classification. Instead, the methods utilize a plurality of tubelets used to represent discreet actions, persons, and objects of interest within the comprehensive untrimmed video segment. The tubelets are localized to correct for pixel-level foreground-background biases, which are then turned into short spatio-temporal action tubelets that are passed to a classification network to obtain multi-label predictions. After classification, the tubelets are be linked together to obtain the final detections with varying lengths, and the method merges the short action tubelets into final action detections.

Abstract: Methods of detecting and categorizing an action in an untrimmed video segment regardless of the scale of the action and the close proximity of other actions. The methods improve upon the prior art which either require trimmed video segments including only a single activity depicted therein, or untrimmed video segments including relatively few actions, persons, or objects of interest, thereby directing the classification. Instead, the methods utilize a plurality of tubelets used to represent discreet actions, persons, and objects of interest within the comprehensive untrimmed video segment. The tubelets are localized to correct for pixel-level foreground-background biases, which are then turned into short spatio-temporal action tubelets that are passed to a classification network to obtain multi-label predictions. After classification, the tubelets are be linked together to obtain the final detections with varying lengths, and the method merges the short action tubelets into final action detections.

Abstract: A system and method for switching one or more User Equipment (UEs) 116A-N from a unicast mode to a broadcast or multicast mode to transmit a streaming media content to the UEs 116A-N is provided. The system includes, an Over-the-top (OTT) platform 104, a CDN 112, the UEs 116A-N, a cellular core network 202, one-to-many offload core 204, an analytics engine 206, a database 208, one-to-many transmitter 210, a real time switching module 212, a Cellular base station 214 and a user specified rules module 222. The analytics engine 206 continuously analyzes real-time and historical data stored in the database 208 to identify the UEs 116A-N that receive a streaming media content through the unicast mode and the streaming media content to be offloaded. An offload is a process by which certain portions of the streaming media content is shifted from the unicast mode to the broadcast or multicast mode.

Abstract: This disclosure relates to utilizing a statistical model trained on character dimensions to determine a likelihood of a person purchasing a product. The method may include obtaining user-input data of a first person (e.g., textual-input data, survey-response data, offer information, or clickstream data associated with a first person). A character profile for the first person is derived using the user-input data and a psycholinguistic lexicon. A statistical model is generated based on the derived character profile of the first person. Second user-input data associated with a second person is obtained. The second user-input is applied to the statistical model to determine an output of the model (e.g., a statistical probability value that quantifies, for example, a predicted intention of the second person to purchase a particular product).

Abstract: This disclosure relates to utilizing a statistical model trained on character dimensions to determine a likelihood of a person purchasing a product. The method may include obtaining user-input data of a first person (e.g., textual-input data, survey-response data, offer information, or clickstream data associated with a first person). A character profile for the first person is derived using the user-input data and a psycholinguistic lexicon. A statistical model is generated based on the derived character profile of the first person. Second user-input data associated with a second person is obtained. The second user-input is applied to the statistical model to determine an output of the model (e.g., a statistical probability value that quantifies, for example, a predicted intention of the second person to purchase a particular product).

Abstract: System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.

Abstract: System and method for identifying erroneous videos and assessing video quality is provided. Feature vectors are generated corresponding to a plurality of frames associated with the one or more videos. The feature vectors are subsequently subjected to anomaly detection to obtain first and second normalized path lengths and normalized anomaly measures. The first and second normalized path lengths and normalized anomaly measures are provided to a regression model to identify the erroneous video.