Video management system

A method, system, and computer program product for creating step-based answers from multimedia content are provided. The method receives a search query including a set of search terms. A set of question characteristics is determined for the search query. The set of question characteristics are based in part on the set of search terms. The method generates a set of steps for an answer to the search query. The set of steps are generated based in part on the set of question characteristics. The method identifies media content relevant to the set of steps and segments the media content to generate a set of media segments. Each media segment of the set of media segments represent a portion of the media content. The method combines the set of media segments based on the set of steps to generate a media answer to the search query.

BACKGROUND

Network resources may contain information useful or desired by users. Users may access such network resources using search engines or search functionality to retrieve information from these network resources. Search engines and functionality often access text-based documents using natural language processing and text analysis operations to match words in a document to keywords of a search query. In searching video or other multimedia content, search engines and functionality often rely on parsing or comparing metadata describing such content in order to match keywords and retrieve relevant search results.

SUMMARY

According to an embodiment described herein, a computer-implemented method for creating step-based answers from multimedia content is provided. The method receives a search query including a set of search terms. A set of question characteristics is determined for the search query. The set of question characteristics are based in part on the set of search terms. The method generates a set of steps for an answer to the search query. The set of steps are generated based in part on the set of question characteristics. The method identifies media content relevant to the set of steps and segments the media content to generate a set of media segments. Each media segment of the set of media segments represent a portion of the media content. The method combines the set of media segments based on the set of steps to generate a media answer to the search query.

According to an embodiment described herein, a system for creating step-based answers from multimedia content is provided. The system includes one or more processors and a computer-readable storage medium, coupled to the one or more processors, storing program instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. The system receives a search query including a set of search terms. A set of question characteristics is determined for the search query. The set of question characteristics are based in part on the set of search terms. The system generates a set of steps for an answer to the search query. The set of steps are generated based in part on the set of question characteristics. The system identifies media content relevant to the set of steps and segments the media content to generate a set of media segments. Each media segment of the set of media segments represent a portion of the media content. The system combines the set of media segments based on the set of steps to generate a media answer to the search query.

According to an embodiment described herein a computer program product for creating step-based answers from multimedia content is provided. The computer program product receives a search query including a set of search terms. A set of question characteristics is determined for the search query. The set of question characteristics are based in part on the set of search terms. The computer program product generates a set of steps for an answer to the search query. The set of steps are generated based in part on the set of question characteristics. The computer program product identifies media content relevant to the set of steps and segments the media content to generate a set of media segments. Each media segment of the set of media segments represent a portion of the media content. The computer program product combines the set of media segments based on the set of steps to generate a media answer to the search query.

DETAILED DESCRIPTION

The present disclosure relates generally to methods for generating search results, but not exclusively, to a computer-implemented method for creating step-based answers to search queries from multimedia content. The present disclosure relates further to a related system for search result generation, and a computer program product for operating such a system.

With the advent of the internet and network-based resources, differing types of media and information are increasingly available to users of search systems. Often search systems use keyword matching to surface results to search queries. Keyword matching search systems work well for search queries which are little more than a cluster of keywords. Some search systems use natural language processing on sentence-based search queries to generate a set of keywords used to retrieve search results. However, search systems often encounter difficulties in retrieving distinct pieces of information where video, audio, and other multimedia resources are accessed during a search. Search systems based on keyword matching often have difficulty in interpreting different types of search queries and an intent of a user relating to the search query. Further, search systems often encounter difficulties in finding distinct pieces of information to complete tasks based on search queries intended to determine procedural information. For example, search engines often have difficulty in finding step by step instructions from one or more video clips distributed across available network resources.

Embodiments of the present disclosure provide methods, systems, and computer program products for creating step-based answers from multimedia content. Some embodiments of the present disclosure enable automated task determinations from search queries. Such task determinations may be based on the search query, user profiles, user skill levels, combinations thereof, and other suitable and relevant information regarding one or more of the user and the search query. Some embodiments of the present disclosure identify relevant portions of one or more multimedia resources, extract the relevant portions of data, and generate new multimedia presentations to address received search queries. These new multimedia presentations may be generated and presented in a manner relative to the search query, a knowledge level of a user, and a skill level of a user. As such, the present disclosure enables extraction and composition of information in logical steps to answer search queries, thereby increasing speed of data acquisition, reducing processing power and processing cycles used to produce desired search results, and improve quality and relevancy of search results.

Some embodiments of the concepts described herein may take the form of a system or a computer program product. For example, a computer program product may store program instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations described above with respect to the computer implemented method. By way of further example, the system may comprise components, such as processors and computer readable storage media. The computer readable storage media may interact with other components of the system to cause the system to execute program instructions comprising operations of the computer implemented method, described herein. For the purpose of this description, a computer-usable or computer-readable medium may be any apparatus that may contain means for storing, communicating, propagating, or transporting the program for use, by, or in connection with, the instruction execution system, apparatus, or device.

Referring now toFIG. 1, a block diagram of an example computing environment100is shown. The present disclosure may be implemented within the example computing environment100. In some embodiments, the computing environment100may be included within or embodied by a computer system, described below. The computing environment100may include a query system102. The query system102may comprise an interface component110, a characteristic component120, a step component130, a content component140, a segment component150, and an answer component160. The interface component110presents user interface elements, receives search queries, and presents answers to the search query generated by the query system102. The characteristic component120determines question characteristics and user characteristics for use in generating answers relating to received search queries. The step component130generates sets of steps for answers to specified search queries. The content component140identifies media content relevant to received search queries and sets of steps generated based on the received search queries. The segment component150divides or otherwise segments accessed media content to generate sets of media segments. The answer component160combines sets of media segments based on sets of steps to generate a media answers to received search queries. Although described with distinct components, it should be understood that, in at least some embodiments, components may be combined or divided, or additional components may be added, without departing from the scope of the present disclosure.

Referring now toFIG. 2, a flow diagram of a computer-implemented method200is shown. The computer-implemented method200is a method for creating step-based answers from multimedia content. In some embodiments, the computer-implemented method200may be performed by one or more components of the computing environment100, as described in more detail below.

At operation210, the interface component110receives a search query. The search query may be received as a portion of text. When received as text, the search query may be received in the form of a text-based message, a text-based entry into a user interface, text within an image, combinations thereof, or any other suitable text. The search query may also be received as an auditory input. When received in an auditory form, the search query may be received as an audio recording, a microphone input, audio data from a video segment, combinations thereof, or any other suitable input format. The search query may be received as an initiating input or as a response to a prompt. The prompt may be a prompt presented within a user interface, instructing a user to enter a search query or ask a question.

In some embodiments, the search query includes a set of search terms. The search terms may be arranged according to a syntax or other organizational structure. For example, the search query may be received in the form of a question. The question may contain a plurality of terms, a portion of which make up the set of search terms. In such instances, the search query may include a plurality of words. A first portion of the plurality of words may be the set of search terms distributed among the plurality of words according to the syntax. A second portion of the plurality of words may be a set of supporting terms distributed among the plurality of words according to the syntax. The first and second portion of words may define query characteristics. Query characteristics may include a query type, a query topic, a query time frame, a document type, and any other suitable and relevant query characteristics or attributes.

In some embodiments, the search query is received from a user. In such instances, the interface component110identifies a profile for the user. The profile of the user may be a social media profile, a local profile, a system profile, combinations thereof, or any other suitable repository of information describing the user. Social media profiles may be accessed by the interface component110upon identification of the profile for the user. The social media profiles may describe or include attributes of the user, provided to a social media application by the user, and information relating to social media posts of the user. The local profile may be a profile for the user on a computing device (e.g., a mobile phone, a laptop computer, a tablet, a desktop computer, etc.). The local profile may describe or include attributes of the user, provided to or generated by the computing device based on activities performed on the computing device and information provided thereto. The system profile may be a profile for the user on the query system102. The system profile may include information provided by the user and determined from activities performed on the query system102.

Profiles for the user may be based on information provided by the user in an opt-in manner and may be maintained until the user opts-out of the profile or access to a system or device used to create, develop, or maintain the profile. Profiles generated for the user may be governed by data sharing permissions defined by the user to maintain a level of user privacy and/or anonymity desired by the user. The user may access and modify information within the profiles related to the user. Once the user opts-out of the profile, information included in the profile is deleted from the relevant systems or devices storing the profile.

In some embodiments, the characteristic component120determines a user skill based on the profile of the user. The user skill may be determined by accessing the profile of the user. The characteristic component120may parse or otherwise analyze the profile of the user using one or more techniques of keyword matching, natural language processing, neural networking, convolutional neural networking, machine learning, combinations thereof, or any other suitable and relevant technique capable of identifying one or more skills and a relevant proficiency for each skill.

In some embodiments, the interface component110passes information to the characteristic component120related to the search query, such as keywords or query characteristics. The characteristic component120may determine one or more skill associated with the user which is relevant to the search query. For example, where the interface component110passes information indicating a physics query and one or more planet names to the characteristic component120, the characteristic component may analyze the profile of the user to determine a relevant skill in physics or astrophysics and a relative level of that skill. For example, the characteristic component120may determine an education level and area of study for the user to generate a predicted skill level of the user in physics or astrophysics. In some embodiments, the characteristic component120determines skills of the user relating to searching or accessing information.

At operation220, the characteristic component120determines a set of question characteristics for the search query. In some embodiments, the set of question characteristics are determined based in part on the set of search terms. The characteristic component120may initially determine a question type for the search query. The question type may define a type of information (e.g., a topic) sought by the user. The question type may also define one or more records sought by the user, a type or name of record author, a time frame (e.g., a start time, an end time, a time duration) for the search query or records to be retrieved, combinations thereof, or any other suitable and relevant characteristic or attribute defining or describing the search query.

In some embodiments, the characteristic component120determines the set of question characteristics using natural language processing, neural networking, machine learning, or any other suitable analysis methodology. In some embodiments, the characteristic component120uses one or more of latent dirichlet allocation (LDA) and biterm topic modeling to determine the set of question characteristics, such as question type or question topic. The characteristic component120may also use LDA and biterm topic modeling on the profile of the user to determine skills relevant to the search query and a level of the relevant skill. The characteristic component120may use biterm topic modeling to generate a topic model for the search query and the profile of the user.

At operation230, the step component130generates a set of steps for an answer to the search query. In some embodiments, the set of steps are generated based in part on the set of question characteristics. Where the set of steps are generated based on the set of question characteristics, the step component130may access a topic model, generated for the search query, and generate the set of steps from the topic model. In embodiments where the characteristic component120determines the user skill and skill level, the step component130generates the set of steps relative to the user skill. In such instances, the step component130accesses a topic model for the search query and a topic model for the user skill and generates the set of steps from the respective topic models. The step component130may generate the set of steps as a set of operations used to answer the question of the search query relative to the user's skill level.

At operation240, the content component140identifies media content relevant to the set of steps. The content component140may identify the media content by accessing a repository of media content. After accessing the repository, the content component140compares metadata in the repository with one or more terms derived from one or more topic models generated for the search query and the user skills. The metadata in the repository describes a set of media content with content keywords. The one or more terms may represent keywords for topics identified by the one or more topic model as describing the search query and the user skills. In some embodiments, the content component140uses keyword matching techniques to determine at least one media content which is relevant to the search query and the set of steps.

In some embodiments, the content component140identifies a plurality of media content as being relevant to the search query and the set of steps. The content component140may determine a relative relevance level for each media content of the plurality of media content. In some embodiments, the relative relevance levels are determined as percentage matches between keywords of the topic model and the metadata for respective media content. The content component140may also determine the relative relevance levels by determining a probability value indicating a probability of whether a respective media content, of the plurality of media content, will answer the search query. The content component140may select a media content with a highest relative relevance as the identified media content relevant to the set of steps.

At operation250, the segment component150segments the media content to generate a set of media segments. In some embodiments, each media segment of the set of media segments represents a portion of the media content. The media segments may be sections or portions of the media content determined to be relevant to one or more steps of the set of steps. For example, where the media content is a video having a first length (e.g., two hours), each segment may be a shorter portion (e.g., five minutes) of the total video.

In some embodiments, once a media content is selected, the content component140triggers the segment component150. The segment component150analyzes the media content to identify at least a portion of the media content to segment. The segment component150may analyze the media content using speech-to-text, image recognition, text recognition (e.g., subtitles, metadata, words appearing within the media content, etc.), machine learning, neural networking, combinations thereof, or any other suitable and relevant information extraction method. Based on the topic model or topic models, the segment component150identifies within the media content a portion of the media content applicable to at least one step of the set of steps. In some instances, the segment component150identifies a portion of the media content for each step of the set of steps. The segment component150may segment the media content by extracting, copying, or otherwise separating the identified portions of the media content from remaining portions of the media content.

In some embodiments, the set of media segments represents a first portion of the media content. The first portion of the media content may be relevant to the answer to the search query. A second portion of the media content may be excluded from the set of media segments. The second portion of media content may include aspects, sections, or portions of the media content determined to be irrelevant to the answer. Irrelevancy may be determined where a portion of the media content falls below a relevancy threshold for one or more of the steps of the set of steps. The second portion of the media contents may be excluded from inclusion in the set of media segments.

At operation260, the answer component160combines the set of media segments based on the set of steps to generate a media answer to the search query. The answer component160may stitch, link, or otherwise connect the set of media segments together to generate the media answer. The answer component160may connect the set of media segments together without intervening portions of the media content. In some embodiments, the set of media segments are combined consecutively into a sequence corresponding to an order for the set of steps to create the media answer. In such embodiments, each step of the set of steps may be numbered or otherwise ordered or arranged to sequentially build the answer to the search query. As each media segment is identified for a respective step of the set of steps, the media segment may be associated with a number or other indication of a position within the sequence.

In embodiments where the first portion of the media content is relevant to the answer and included in the set of media segments and the second portion is determined to fall below the relevancy threshold, the answer component160excludes the second portion of the media contents from the media answer.

At operation270, the interface component110presents the media answer within a user interface. In some embodiments, each media segment of the media answer is presented in the sequence corresponding to the order for the set of steps. The interface component110may present the media answer in a user interface, such as a multimedia player or application. The interface component110may initially present the media answer as a link or representation. The interface component110may receive or identify a selection of the link and initiate playback or presentation of the media answer. In some embodiments, once the media answer is generated by the answer component160, the interface component110may generate and insert labels or other indications within the media answer. In such instances, each segment of the set of segments may be labeled indicating a step of the set of steps or a position within the sequence. While presenting the media answer, labels describing each segment may be presented prior to presentation of the respective segment described by the label.

FIG. 3shows a flow diagram of an embodiment of a computer-implemented method300for creating step-based answers from multimedia content. The method300may be performed by or within the computing environment100. In some embodiments, the method300comprises or incorporates one or more operations of the method200. In some instances, operations of the method300may be incorporated as part of or sub-operations of the method200.

In operation310, the characteristic component120generates a topic model based in part on a question type of the search query. The question type may be a question characteristic of the set of question characteristics. Question types may represent a topic of the question, a category of the question, or any other question type describing or defining the question. In some embodiments, the characteristic component120generates the topic model based on the question type and a user profile. The set of steps for the answer may be generated based on the question type and the topic model.

In operation320, the content component140maps the media content to identify relevant portions of the media content. In some embodiments, the content component140maps the media content by comparing terms within the topic model to a media content repository. The media content repository may contain a plurality of distinct media content and metadata describing each media content instance. The content component140may map the media content prior to receiving the search query, identifying media content, and segments within each instance of media content, which may be associated or representative of specified terms or keywords. In some embodiments, the content component140maps the media content in response to receiving the search query. Once mapped, the content component140may retrieve media content (e.g., a video file) determined to be relevant to the search query based on terms, topics, or a query type of the search query.

In operation330, the segment component150assigns each media segment of the set of media segments to a step of the set of steps. In some embodiments, each media segment is assigned to a step based on the media segment being above a threshold level of relevancy for the step. As described above, the segment component150may identify each media segment from the selected media content. The segment component150may determine a relevancy of each media segment, relative to one or more steps, based on a comparison of the content, topic, keywords, or depictions of the media segment to keywords or topic models for the respective step. The segment component150may then separate each media segment from the media content and associate each media segment with a step for which its relevance is determined to be a high or a match. The segment component150may then pass the set of media segments to the answer component160to generate the media answer.

Embodiments of the present disclosure may be implemented together with virtually any type of computer, regardless of the platform being suitable for storing and/or executing program code.FIG. 4shows, as an example, a computing system400(e.g., cloud computing system) suitable for executing program code related to the methods disclosed herein and for creating step-based answers from multimedia content.

As shown in the figure, computer system/server400is shown in the form of a general-purpose computing device. The components of computer system/server400may include, but are not limited to, one or more processors402(e.g., processing units), a system memory404(e.g., a computer-readable storage medium coupled to the one or more processors), and a bus406that couple various system components including system memory404to the processor402. Bus406represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, and not limiting, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnects (PCI) bus. Computer system/server400typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer system/server400, and it includes both, volatile and non-volatile media, removable and non-removable media.

The program/utility, having a set (at least one) of program modules416, may be stored in the system memory404by way of example, and not limiting, as well as an operating system, one or more application programs, other program modules, and program data. Program modules may include one or more of the interface component110, the characteristic component120, the step component130, the content component140, the segment component150, and the answer component160, which are illustrated inFIG. 1. Each of the operating systems, one or more application programs, other program modules, and program data or some combination thereof, may include an implementation of a networking environment. Program modules416generally carry out the functions and/or methodologies of embodiments of the present disclosure, as described herein.

The computer system/server400may also communicate with one or more external devices418such as a keyboard, a pointing device, a display420, etc.; one or more devices that enable a user to interact with computer system/server400; and/or any devices (e.g., network card, modem, etc.) that enable computer system/server400to communicate with one or more other computing devices. Such communication can occur via Input/Output (I/O) interfaces414. Still yet, computer system/server400may communicate with one or more networks such as a local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter422. As depicted, network adapter422may communicate with the other components of computer system/server400via bus406. It should be understood that, although not shown, other hardware and/or software components could be used in conjunction with computer system/server400. Examples include, but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

Deployment models may include private cloud, community cloud, public cloud, and hybrid cloud. In private cloud, the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises. In community cloud, the cloud infrastructure is shared by several organizations and supports specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party that may exist on-premises or off-premises. In public cloud, the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services. In hybrid cloud, the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).

Cloud models may include characteristics including on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. In on-demand self-service a cloud consumer may unilaterally provision computing capabilities such as server time and network storage, as needed automatically without requiring human interaction with the service's provider. In broad network access, capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs). In resource pooling, the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter). In rapid elasticity, capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time. In measured service, cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.

The corresponding structures, materials, acts, and equivalents of all means or steps plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements, as specifically claimed. The description of the present disclosure has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the present disclosure in the form disclosed. Many modifications and variations will be apparent to those of ordinary skills in the art without departing from the scope of the present disclosure. The embodiments are chosen and described in order to explain the principles of the present disclosure and the practical application, and to enable others of ordinary skills in the art to understand the present disclosure for various embodiments with various modifications, as are suited to the particular use contemplated.