A system for detecting errors in task workflows from a real time video feed records. The video feed that shows a plurality of steps being performed to accomplish a plurality of tasks through an automation process system. The system splits the video feed into a plurality of video recordings which are valid breakpoints determined through cognitive Machine Learning Engine, where each video recording shows a single task. For each task from among the plurality of tasks, the system determines whether the task fails and the exact point of failure for that task. If the system determines that the task fails, the system determines a particular step where the task fails. The system flags the particular step as a failed step. The system reports the flagged step for troubleshooting.

Systems and methods for generating, administering and analyzing a user experience study are provided. In particular, intents can be generated from a user experience study by applying one or more screener questions to participants and subjecting the screened participants to one or more tasks. Corresponding clickstreams and success data for each participant engaging in the tasks can be recorded. The success and clickstream data can also be aggregated for all the screened participants as aggregated results. Video data including audio for each of the screened participants can also be recorded.

A video processing system includes: an object movement information acquiring means for detecting a moving object moving in a plurality of segment regions from video data obtained by shooting a monitoring target area, and acquiring movement segment region information as object movement information, the movement segment region information representing segment regions where the detected moving object has moved; an object movement information and video data storing means for storing the object movement information in association with the video data corresponding to the object movement information; a retrieval condition inputting means for inputting a sequence of the segment regions as a retrieval condition; and a video data retrieving means for retrieving the object movement information in accordance with the retrieval condition and outputting video data stored in association with the retrieved object movement information, the object movement information being stored by the object movement information and video data storing means.

Methods, apparatus, systems and articles of manufacture are disclosed for scalable architectures for reference signature matching and updating. An example method for scalable architectures for reference signature matching and updating includes accessing site signatures to be compared to reference signatures from a first group of media sources. Determining if a first reference node is an owner of a first one of the site signatures. Comparing a neighborhood of site signatures including the first site signature to reference signatures in a first subset of reference signatures when the first reference node is the owner of the first site signature, the first subset of references signatures stored in a first memory partition associated with the first reference node. Not comparing site signature to reference signatures when the first reference node is not the owner of the first one of the site signatures.

Methods, apparatus, systems and articles of manufacture are disclosed for scalable architectures for reference signature matching and updating. An example method for scalable architectures for reference signature matching and updating includes accessing site signatures to be compared to reference signatures from a first group of media sources. Determining if a first reference node is an owner of a first one of the site signatures. Comparing a neighborhood of site signatures including the first site signature to reference signatures in a first subset of reference signatures when the first reference node is the owner of the first site signature, the first subset of references signatures stored in a first memory partition associated with the first reference node. Not comparing site signature to reference signatures when the first reference node is not the owner of the first one of the site signatures.

A computer-implemented method executed by at least one processor for performing mini-batching in deep learning by improving cache utilization is presented. The method includes temporally localizing a candidate clip in a video stream based on a natural language query, encoding a state, via a state processing module, into a joint visual and linguistic representation, feeding the joint visual and linguistic representation into a policy learning module, wherein the policy learning module employs a deep learning network to selectively extract features for select frames for video-text analysis and includes a fully connected linear layer and a long short-term memory (LSTM), outputting a value function from the LSTM, generating an action policy based on the encoded state, wherein the action policy is a probabilistic distribution over a plurality of possible actions given the encoded state, and rewarding policy actions that return clips matching the natural language query.

The described technology is generally directed an extension to the IFS domains architecture, referred to herein as filter domains. IFS domains allows tagging of files in a tree-like dataset. Thus, a domain can be defined at the root of the dataset such as the topmost directory under which all files reside. These domains are inherently hierarchichal, path-based entities. Filter domains extends this organization to allow domains to be applied beyond hierarchical tree structures in order to also provide arbitrary grouping of file objects based on any suitable membership condition.

An example method may include receiving, at a computing device, a digital image associated with a particular media content program, the digital image containing one or more faces of particular people associated with the particular media content program. A computer-implemented face recognition program together with a set of computational models associated with the particular media content program may be applied to the digital image to recognize one or more of the particular people in the digital image, together with respective geometric coordinates for each of the one or more detected faces. At least a subset of the set of the computational models may be associated with a respective one of the particular people. The digital image together may be stored in non-transitory computer-readable memory, together with information assigning respective identities of the recognized particular people, and associating with each respective assigned identity geometric coordinates in the digital image.

A media file processing method includes: recognizing content features of a target media file, wherein the content features include an image feature and/or a sound feature; determining a target aggregation theme of the target media file according to the recognized content features of the target media file; determining the target media file as media files under the target aggregation theme; and synthesizing the media files under the target aggregation theme in response to a video clip instruction with respect to the target aggregation theme, to obtain a target video corresponding to the target aggregation theme.

The field of the invention relates to methods, systems and computer program products for navigating digital media content, in particular for navigating digital media content using an interface abstracted from that digital media content instead of, or as a supplement to, traditional user interface controls. There is provided a method for presenting a user interface to an end user to facilitate the searching, browsing and/or navigation of digital media content, the method comprising the steps of: (a) analysing the digital media content to create “hooks” related to the digital media content, or retrieving “hooks” in the digital media content, and (b) replacing or augmenting a graphical or textual representation of the digital media content with the “hooks.”