Embodiments related to the use of feedback to adapt digital video recording are disclosed. For example, one disclosed embodiment provides, on a computing device, a method of adapting an identification and scheduling of video items for recording based on usage. The method comprises detecting a trigger to perform a series update, and determining one or more video items to record as a part of a series by providing input to a series update module that returns candidate video items for inclusion in the series. The method further comprises scheduling zero or more of the candidate video items for recording, receiving feedback related to video item playback of one or more video items that were recorded based upon the scheduling, and based upon the feedback, adapting the input provided to the series update module for a later series update.

An adaptive action recognizer for video that performs multiscale spatiotemporal decomposition of video to generate lower complexity video. The adaptive action recognizer has a number of processing pathways, one for each level of video complexity with each processing pathway having a different computational cost. The adaptive action recognizer applies a decision making scheme that encourages using low average computational costs while retaining high accuracy.

A system having an adaptive browse feature and an adaptive flip feature is provided. The adaptive browse and flip features may be selected to receive program viewing suggestions. The system may provide a suggestion by displaying an adaptive browse region or adaptive flip region including a program suggestion. The system identifies programs to suggest based on a user=s viewing activity. The system uses different algorithms that are user-selectable and user-adjustable to identify program suggestions. The system may query a program guide database to build a list of programs having attributes similar to the attributes of the current program or the last viewed program. The system may use an adaptive learning algorithm such as a neural network. The neural network may be trained by the program guide by monitoring user-viewing activity. Each algorithm may be personalized for multiple users.

Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically detecting and rendering highlights from streaming videos in real-time. As a streaming video is being broadcast over the Internet, the disclosed systems and methods determine each type of scene from the streaming video, and automatically score highlight scenes. The scored highlight scenes are then communicated to users as compiled video segments, which can be over any type of channel or platform accessible to a user's device and network that enables content rendering and user interaction.

Aspects of the subject disclosure may include, for example, identifying content consumption data associated with media content consumption at a customer device, and generating a content selection recommendation for the customer device. Some embodiments can include determining a habit-based content selection vector for the customer device. Various embodiments can include determining the habit-based content selection vector based on a habit profile for the customer device. Some embodiments can include adjusting a content selection vector for the customer device based on the habit-based content selection vector for the customer device. Various embodiments can include generating the content selection recommendation for the customer device based on the adjusted content selection vector. Other embodiments are disclosed.

In certain embodiments, training of a neural network or other prediction model may be facilitated via live stream concept association. In some embodiments, a live video stream may be loaded on a user interface for presentation to a user. A user selection related to a frame of the live video stream may be received via the user interface during the presentation of the live video stream on the user interface, where the user selection indicates a presence of a concept in the frame of the live video stream. In response to the user selection related to the frame, an association of at least a portion of the frame of the live video stream and the concept may be generated, and the neural network or other prediction model may be trained based on the association of at least the portion of the frame with the concept.

The application relates to a video recommendation method and device, and a computer readable storage medium. The video recommendation method comprises: obtaining a user feature of a sample user and a video feature of a sample video; learning a click rate, a like rate, and a follow rate on the basis of a full-connection neural network algorithm to obtain trained user feature and video feature; performing, according to the trained user feature and video feature, combined learning on the click rate, the like rate, and the follow rate on a user side neural network and a video side neural network; and obtaining a video recommendation list according to a network parameter of a neural network algorithm obtained by means of combined learning. According to the video recommendation method, by adding a full-connection neural network algorithm training phase, the trained user feature and video feature are obtained.

Disclosed herein are system, method, and computer program product embodiments for detecting human presence in front of a plurality of sensors, such as speaker sensors, and a device with a processor, such as a television. An example method includes varying, during a collection routine, a respective signal strength of one or more of a plurality of transmitters. The example method further includes receiving results of the collection routine in a form of raw data from a plurality of sensors. The example method further includes determining, by at least one processor, a respective geographical position of one or more humans present within a predetermined geographical range of the at least one processor based on the raw data from the plurality of sensors. Subsequently, the example method includes executing an action based on the respective geographical position of the one or more humans.

The present invention provides a robust and effective solution to an entity or an organization by enabling a plurality of set top boxes (STBs) to be used as seeders for peer to peer network distribution of data. The plurality of STBs may be used to utilize the Internet for downloading content of the network along with streaming of the content. After downloading the content by the STB by following a predefined set of instructions, the STB may provide the downloaded content to the network. After a predefined time, the STB would not be able to transfer data via internet because a sever coupled to the STB may automatically stop accepting incoming connections to download from other STBs associated with the network.

Exemplary embodiments are directed to an apparatus dedicated to controlling a specified multimedia device for viewing programming content. The apparatus has an input interface configured for capturing a fingerprint of a user. The apparatus also includes memory for storing a plurality of user identification files. Each user identification file including data associated with at least one reference fingerprint of a respective user. A processor compares the captured fingerprint with at least one reference fingerprint stored in memory, and generates a control signal based on a result of the comparison. The control signal can include user profile identifier associated with the captured fingerprint and command data for instructing the multimedia device to generate a customized or default user interface for the identified user to view or access programming content. The control signal is transmitted to a multimedia device.