A computer-implemented method of identifying whether a target video comprises an abridged version of a reference video includes evaluating condition a) that the target video does not comprise all shots of the reference video; condition b) that the target video includes groups of consecutive shots also included in the reference video; and condition c) that all shots which are present in both the target video and the reference video are in the same order. The method further includes identifying whether the target video comprises an abridged version of the reference video; and outputting a result of the identifying. The target video is identified as comprising an abridged version of the reference video on condition that conditions a), b) and c) are met. Also provided is a data processing apparatus for performing the method; and a computer program and computer readable storage medium comprising instructions to perform the method.

A computer-implemented method of identifying whether a target video comprises an abridged version of a reference video includes evaluating condition a) that the target video does not comprise all shots of the reference video; condition b) that the target video includes groups of consecutive shots also included in the reference video; and condition c) that all shots which are present in both the target video and the reference video are in the same order. The method further includes identifying whether the target video comprises an abridged version of the reference video; and outputting a result of the identifying. The target video is identified as comprising an abridged version of the reference video on condition that conditions a), b) and c) are met. Also provided is a data processing apparatus for performing the method; and a computer program and computer readable storage medium comprising instructions to perform the method.

A video search system includes: an object tag acquisition unit that obtains an object tag associated with an object that appears in a video; a search query acquisition unit that obtains a search query; a similarity calculation unit that calculates a similarity degree between the object tag and the search query; and a video search unit that searches for a video corresponding to the search query on the basis of the similarity degree. According to such a video search system, it is possible to properly recognize the video, for example, by using the search query using a natural language.

A video search system includes: an object tag acquisition unit that obtains an object tag associated with an object that appears in a video; a search query acquisition unit that obtains a search query; a similarity calculation unit that calculates a similarity degree between the object tag and the search query; and a video search unit that searches for a video corresponding to the search query on the basis of the similarity degree. According to such a video search system, it is possible to properly recognize the video, for example, by using the search query using a natural language.

Aspects described provide systems and methods that relate generally to image analysis and, more specifically, identifying individual components and elements in an image. The systems and methods include a valuation application executing one or more application program interfaces (APIs) communicating with one or more websites via a network, where the user is prompted to enter information and/or take pictures or videos of their vehicle that they would like to sell. The valuation application utilizes a machine learning model to identify and value the various vehicle components within the images and videos. Based on the machine learning model, the valuation application identifies each component according to the images and videos and performs a search to determine the value of the components identified. The valuation application tabulates and summarizes the vehicle component resale values and resell information for the user to view.

According to one implementation, a system includes a computing platform having processing hardware, a system memory storing a software code; and a machine learning model based classifier. The processing hardware is configured to execute the software code to receive tagging quality assurance (QA) data including multiple terms applied as tags and corrections to those tags, to identify, using the tagging QA data, a first problematic term, and to classify, using the machine learning model based classifier, the first problematic term as one of confusing or flawed. The processing hardware is further configured to execute the software code to obtain, when the first problematic term is classified as confusing, a comparative sample for clarifying use of the first problematic term, and to obtain, when the first problematic term is classified as flawed, modification data for editing a predetermined annotation taxonomy including the first problematic term.

A system for classifying tasks based on workflow patterns detected on workflows through a real time video feed that shows steps being performed to accomplish a plurality of tasks. Each task is associated with a different set of steps. The system accesses a first set of steps known to be performed to accomplish a first task on the webpages. The first set of steps is represented by a first set of metadata. The system extracts a second set of metadata from the video feed. The second set of metadata represents a second set of steps to perform a second task. The system determines whether the second set of metadata corresponds to the first set of metadata. If it is determined that the second set of metadata corresponds to the first set of metadata, the system classifies the second task in a class to which the first task belongs.

Aspects relate to apparatuses and methods for parsing and comparing resume video duplications. An exemplary apparatus includes a memory communicatively connected to at least a processor and includes instructions configuring the at least a processor to acquire a plurality of video elements from an existing video resume, wherein the existing video resume includes at least an image component, recognize subject-specific data of the existing video resume as a function of the at least an image component, wherein the subject-specific data includes verbal content and non-verbal content, recognize at least a keyword of the existing video resume as a function of the subject-specific data, recognize at least a feature of the existing video resume as a function of the subject-specific data, compare the subject-specific data to a target video resume and determine, as a function of the comparison result, a duplication coefficient for the target resume video.

Aspects relate to apparatuses and methods for parsing and comparing resume video duplications. An exemplary apparatus includes a memory communicatively connected to at least a processor and includes instructions configuring the at least a processor to acquire a plurality of video elements from an existing video resume, wherein the existing video resume includes at least an image component, recognize subject-specific data of the existing video resume as a function of the at least an image component, wherein the subject-specific data includes verbal content and non-verbal content, recognize at least a keyword of the existing video resume as a function of the subject-specific data, recognize at least a feature of the existing video resume as a function of the subject-specific data, compare the subject-specific data to a target video resume and determine, as a function of the comparison result, a duplication coefficient for the target resume video.

A data recovery method includes receiving a request to recover target audio and video behavior data generated during use of an audio and video application by a target user. The target audio and video behavior data has been deleted from a database. The method includes obtaining a target data category of the target audio and the video behavior data; searching a blockchain system for the target audio and video behavior data based on the target data category, the blockchain system being configured to store operation data generated by the audio and video application that includes first operation data of audio and video behavior data. The method includes storing the target audio and video behavior data in the database; and returning the target audio and video behavior data to the target user.