Content processing automation

The timely distribution of news or other information describing dynamic or highly complex events often requires contributions from a geographically dispersed team of journalists, as well as sometimes remote experts having specialized knowledge and/or uniquely relevant experience. Although tools for enabling collaboration exist, those conventional tools are typically optimized for a particular project, e.g., a specific news broadcast or story, or for a specific content distribution platform. There remains a need in the art for a collaboration solution enabling the real-time, accurate, and consistent distribution of information provided by multiple contributors and/or content sources, across a variety of communications platforms.

There are provided systems and methods for content processing automation, substantially as shown in and/or described in connection with at least one of the figures, and as set forth more completely in the claims.

FIG. 1 shows a diagram of a an exemplary content processing system, according to one implementation;

FIG. 2 shows another exemplary implementation of a content processing system;

FIG. 3 is a flowchart presenting an exemplary method for use by a content processing system, according to one implementation; and

FIG. 4 shows an exemplary diagram of a content classification software code suitable for execution by a hardware processor of the systems shown by FIGS. 1 and 2 , according to one implementation.

The following description contains specific information pertaining to implementations in the present disclosure. One skilled in the art will recognize that the present disclosure may be implemented in a manner different from that specifically discussed herein. The drawings in the present application and their accompanying detailed description are directed to merely exemplary implementations. Unless noted otherwise, like or corresponding elements among the figures may be indicated by like or corresponding reference numerals. Moreover, the drawings and illustrations in the present application are generally not to scale, and are not intended to correspond to actual relative dimensions.

It is noted that, as used in the present application, the terms “automation,” “automated”, and “automating” refer to systems and processes that do not require human intervention. Although, in some implementations, a human editor or annotator may review or even modify a subject matter classification assignment made by the automated systems and according to the automated methods described herein, that human involvement is optional. Thus, the methods described in the present application may be performed under the control of hardware processing components of the disclosed systems.

It is further noted that, as defined in the present application, an artificial neural network, or ANN, is a machine learning engine designed to progressively improve its performance of a specific task. In various implementations, ANNs may be utilized to perform natural-language processing.

FIG. 1 shows a diagram of exemplary content processing system 100 , according to one implementation. As shown in FIG. 1 , content processing system 100 is utilized within a collaboration environment including communication network 140 , system users 144 including exemplary users 144 a and 144 b , and multiple sources of content including news wires 150 a , social media platforms 150 b , video source 150 c , and crowd source 150 d . Also shown in FIG. 1 are user device 130 a utilized by user 144 a , user device 130 b utilized by user 144 b , news feed 152 a provided by news wires 150 a , social media content 152 b provided by social media platforms 150 b , video content 152 c provided by video source 150 c , and crowd sourced content 152 d provided by crowd source 150 d.

Content processing system 100 includes computing platform 102 having hardware processor 104 , and system memory 106 implemented as a non-transitory storage device. As further shown in FIG. 1 , system memory 106 may store collaboration application 110 , reference database 112 , natural language processor 114 including ANN 116 , computer vision analyzer 118 , and content classification software code 120 . In addition, FIG. 1 shows network communication links 142 interactively connecting user devices 130 a and 130 b with computing platform 102 via communication network 140 , content input or inputs 152 e (hereinafter “content input(s) 152 e ”) provided by one or more of users 144 a and 144 b , and information 154 received by one or more of users 144 a and 144 b.

It is noted that, although the present application refers to collaboration application 110 , reference database 112 , natural language processor 114 including ANN 116 , computer vision analyzer 118 , and content classification software code 120 as being stored in system memory 106 for conceptual clarity, more generally, system memory 106 may take the form of any computer-readable non-transitory storage medium. The expression “computer-readable non-transitory storage medium,” as used in the present application, refers to any medium, excluding a carrier wave or other transitory signal that provides instructions to a hardware processor of a computing platform, such as hardware processor 104 of computing platform 102 . Thus, a computer-readable non-transitory medium may correspond to various types of media, such as volatile media and non-volatile media, for example. Volatile media may include dynamic memory, such as dynamic random access memory (dynamic RAM), while non-volatile memory may include optical, magnetic, or electrostatic storage devices. Common forms of computer-readable non-transitory media include, for example, optical discs, RAM, programmable read-only memory (PROM), erasable PROM (EPROM), and FLASH memory.

It is further noted that although FIG. 1 depicts collaboration application 110 , reference database 112 , natural language processor 114 including ANN 116 , computer vision analyzer 118 , and content classification software code 120 as being mutually co-located in system memory 106 , that representation is also merely provided as an aid to conceptual clarity. More generally, content processing system 100 may include one or more computing platforms, such as computer servers for example, which may be co-located, or may form an interactively linked but distributed system, such as a cloud based system, for instance. As a result, hardware processor 104 and system memory 106 may correspond to distributed processor and memory resources within content processing system 100 . Thus, it is to be understood that any or all of collaboration application 110 , reference database 112 , natural language processor 114 including ANN 116 , computer vision analyzer 118 , and content classification software code 120 may be stored remotely from one another within the distributed memory resources of content processing system 100 .

Although each of user devices 130 a and 130 b is shown as a personal communication device in the form of a smartphone or tablet computer in FIG. 1 , that representation is also provided merely as an example. More generally, user devices 130 a and 130 b may be any suitable mobile or stationary computing device or system remote from computing platform 102 , and capable of performing data processing sufficient to provide a user interface, support connections to network 140 , and implement the functionality ascribed to user devices 130 a and 130 b herein. For example, in other implementations, user devices 130 a and 130 b may take the form of laptop or desktop computers, for example.

According to the implementation shown in FIG. 1 , users 144 a and 144 b may utilize respective user devices 130 a and 130 b to interact with content processing system 100 over communication network 140 , for example to download collaboration application 110 to user devices 130 a and 130 b , to provide content input(s) 152 e to content classification software code 120 , and/or to retrieve information 154 from reference database 112 . In one such implementation, computing platform 102 may correspond to one or more web servers, accessible over a packet-switched network such as the Internet, for example. Alternatively, computing platform 102 may correspond to one or more computer servers supporting a wide area network (WAN), a local area network (LAN), or included in another type of private or limited distribution network.

Content processing system 100 may be utilized in a wide variety of content classification and management applications. Nevertheless, by way of example, the implementation shown in FIG. 1 will be further described with reference to news gathering and editing activities. In such an implementation, content processing system 100 may be utilized as a web-based collaboration resource for news organizations with multiple shows across multiple platforms. In some implementations, content processing system 100 may provide collaboration application 110 , which may be downloaded to user devices 130 a and 130 b as a mobile application having a social media site look and feel, thereby making it familiar and intuitive to users 144 a and 144 b.

Content processing system 100 breaks down conventional barriers to collaboration by providing one place for all of users 144 to collaborate and share information 154 relevant to story development. For example, content processing system 100 may be developed from the perspective of journalist users 144 , rather than a news show production perspective. That is to say, on a given day, user 144 a may be a journalist chasing one specific event and researching various angles to find the story. Under those circumstances, user 144 a is focused on vetting details about the event of interest and does not want to be distracted by unrelated information.

Content processing system 100 is configured to maintain a single source of editorial information across a newsgathering operation, and to present that information in an effective way for internal collaboration and story generation. Content processing system 100 provides a method for editorial staff worldwide to contribute information as content input(s) 152 e , display information 154 including assignments and locations, and improve accuracy of and accessibility to the most current information 154 . As a result, content processing system 100 can be used to substantially minimize the time for a news organization to turn around a story, while concurrently enhancing the richness of content being reported.

As a story evolves it is clear who is contributing to the story and for what show or platform. Shows, people, and roles may be assigned so that the entire news organization knows who is doing what. Updates are chronological and can be tagged so that journalist users among users 144 can quickly assess what's reportable and what isn't. Show producer users among users 144 can monitor the progress of a given story and assign tasks or follow-ups as necessary.

Because content processing system 100 is configured to be story focused and not show or platform focused, it aligns with the way journalists prefer to work in discovery. When one of users 144 receives information about a news event, the user inputs the information to content processing system 100 as content input(s) 152 e , and that information can become associated with that specific news event in reference database 112 for the entire news division to view, comment on, or update. Users 144 can select a story via collaboration application 110 and may see all information associated with that story with one click.

Referring to FIG. 2 , FIG. 2 shows another exemplary implementation of a content processing system as content processing system 200 . In addition to content processing system 200 , the collaboration environment shown in FIG. 2 includes user devices 230 a and 230 b interactively connected to content processing system 200 over network communication links 242 . Also shown in FIG. 2 are content inputs 252 a , 252 b , 252 c , and 252 d received by content processing system 200 , content input(s) 252 e received by content processing system 200 from user device 230 a , and information 254 provided to user device 230 b by content processing system 200 .

As shown in FIG. 2 , content processing system 200 includes computing platform 202 having hardware processor 204 , and system memory 206 implemented as a non-transitory storage device. As further shown in FIG. 2 , system memory 206 may store collaboration application 210 , reference database 212 , natural language processor 214 including ANN 216 , computer vision analyzer 218 , and content classification software code 220 . As further shown in FIG. 2 , user device 230 a includes display 232 a , hardware processor 234 a , and memory 236 a storing collaboration application 210 a , while user device 230 b includes display 232 b , hardware processor 234 b , and memory 236 b storing collaboration application 210 b.

Network communication links 242 , information 254 , and content inputs 252 a , 252 b , 252 c , 252 d , and 252 e correspond respectively in general to network communication links 142 , information 154 , news feed 152 a , social media content 152 b , video content 152 c , crowd sourced content 152 d , and content input(s) 152 e , in FIG. 1 . Moreover, content processing system 200 including computing platform 202 having hardware processor 204 and system memory 206 , in FIG. 2 , corresponds in general to content processing system 100 including computing platform 102 having hardware processor 104 and system memory 106 , in FIG. 1 . Thus, content processing system 200 may share any of the characteristics attributed to content processing system 100 by the present disclosure, and vice versa.

In addition, reference database 212 , natural language processor 214 including ANN 216 , computer vision analyzer 218 , and content classification software code 220 , in FIG. 2 , correspond respectively in general to reference database 112 , natural language processor 114 including ANN 116 , computer vision analyzer 118 , and content classification software code 120 , in FIG. 1 . In other words, reference database 212 , natural language processor 214 including ANN 216 , computer vision analyzer 218 , and content classification software code 220 may share any of the characteristics attributed to respective reference database 112 , natural language processor 114 including ANN 116 , computer vision analyzer 118 , and content classification software code 120 by the present disclosure, and vice versa.

User devices 230 a and 230 b correspond in general to respective user devices 130 a and 130 b , in FIG. 1 . Moreover, collaboration application 210 / 210 a / 210 b , in FIG. 2 , corresponds in general to collaboration application 110 , in FIG. 1 . As a result, user device 230 a , user device 230 b , and collaboration application 210 / 210 a / 210 b , may share any of the characteristics attributed to respective user device 130 a , user device 130 b , and collaboration application 110 by the present disclosure, and vice versa.

According to the exemplary implementation shown in FIG. 2 , collaboration application 210 a is located in memory 236 a of user device 130 a / 230 a and collaboration application 210 b is located in memory 236 b of user device 130 b / 230 b , collaboration applications 210 a and 210 b having been received from content processing system 100 / 200 via network communication links 142 / 242 . In one implementation, network communication links 142 / 242 correspond to transfer of collaboration applications 210 a and 210 b over a packet-switched network, for example. Once transferred, for instance by being downloaded over network communication links 142 / 242 , collaboration applications 210 a and 210 b may be persistently stored in respective memories 236 a and 236 b , and may be executed locally on respective user devices 130 a / 230 a and 130 b / 230 b by respective hardware processors 234 a and 234 b.

Hardware processors 234 a and 234 b may be the central processing units (CPUs) for respective user devices 130 a / 230 a and 130 b / 230 b , for example, in which role hardware processors 234 a and 234 b run the respective operating systems for user devices 130 a / 230 a and 130 b / 230 b , and execute respective collaboration applications 210 a and 210 b . Displays 232 a and 232 b may take the form of liquid crystal displays (LCDs), light-emitting diode (LED) displays, organic light-emitting diode (OLED) displays, or any suitable display screens that perform a physical transformation of signals to light.

In the exemplary implementation represented in FIG. 2 , users 144 a and 144 b of respective user devices 130 a / 230 a and 130 b / 230 b can utilize respective collaboration applications 210 a and 210 b to send content input(s) 152 e / 252 e to content processing system 100 / 200 and/or to receive information 154 / 254 from content processing system 100 / 200 , via network communication links 142 / 242 . For example, in an exemplary implementation in which users 144 a and 144 b are collaborating on developing a news story, content input(s) 152 e / 252 e may be a journalistic or expert analysis contributed by user 144 a . Furthermore, content inputs 252 a , 252 b 252 c , and 252 d may be contributions to a knowledge base of the developing news story stored in reference database 112 / 212 . In that use case, information 154 / 254 may be an update to the collaborative news story based on any or all of content inputs 252 a , 252 b , 252 c , 252 d , and 252 e.

The functionality of content classification software code 120 / 220 will be further described by reference to FIG. 3 in combination with FIGS. 1, 2, and 4 . FIG. 3 shows flowchart 360 presenting an exemplary method for use by content processing system 100 / 200 , according to one implementation. With respect to the method outlined in FIG. 3 , it is noted that certain details and features have been left out of flowchart 360 in order not to obscure the discussion of the inventive features in the present application.

FIG. 4 shows exemplary content classification software code 420 suitable for execution by hardware processor 104 / 204 of computing platform 102 / 202 , according to one implementation. As shown in FIG. 4 , content classification software code 420 may include metadata parsing module 422 , content analysis module 424 , annotation module 426 , and classification module 428 . In addition, FIG. 4 shows content inputs 452 including exemplary content input 452 n , metadata 472 , language-based content 474 transferred to natural language processor 414 for interpretation, and visual content 478 transferred to computer vision analyzer 418 for interpretation. Also shown in FIG. 4 are description 476 of language-based content 474 obtained from natural language processor 414 , description 480 of visual content 478 obtained from computer vision analyzer 418 , predetermined annotation tags 482 assigned to content input 452 n , and subject matter classification 484 to which content input 452 n is assigned by content classification software code 420 .

Natural language processor 414 , computer vision analyzer 418 , and content inputs 452 correspond respectively in general to natural language processor 114 / 214 , computer vision analyzer 118 / 218 , and any or all of content inputs 252 a , 252 b 252 c , 252 d , and 252 e , in FIG. 2 . In other words, like natural language processor 114 / 214 , natural language processor 414 may include a feature corresponding to ANN 116 / 216 . Moreover, content input 452 n corresponds to any one of content inputs 252 a , 252 b 252 c , 252 d , and 252 e.

Content classification software code 420 corresponds in general to content classification software code 120 / 220 , in FIGS. 1 and 2 , and those corresponding features may share the characteristics attributed to any of the corresponding features by the present disclosure. That is to say, like content classification software code 420 , content classification software code 120 / 220 may include modules corresponding to exemplary metadata parsing module 422 , content analysis module 424 , annotation module 426 , and classification module 428 .

Referring now to FIG. 3 in combination with FIGS. 1, 2, and 4 , flowchart 360 begins with receiving multiple content inputs 452 from multiple content sources (action 361 ). For example, as shown in FIG. 1 , multiple content inputs 452 may include news feed content input 152 a / 252 a provided by or obtained from news wires 150 a . In addition, or alternatively, content inputs 452 may include social media content input 152 b / 252 b provided by or obtained from social media platforms 150 b . In addition, or alternatively, content inputs 452 may include video content input 152 c / 252 c provided by or obtained from video source 150 c / 150 d , and/or crowd sourced content input 152 d / 252 d provided by or obtained from crowd source 150 d . Content inputs 452 may be received from any or all of news wires 150 a , social media platforms 150 b , video source 150 c , crowd source 150 d , and users 144 by content classification software code 120 / 220 / 420 , executed by hardware processor 104 / 204 .

Content inputs 452 may also include content input(s) 152 e / 252 e received from one or both of collaboration applications 210 a and 210 b due to contributions to the collaborative project by respective users 144 a and 144 b . For example, user 144 a and/or 144 b may change the knowledge base of the collaborative project by adding information, deleting misinformation, or revising existing information about the collaborative project, using respective collaboration applications 220 a and 220 b.

It is noted that, due the collaboration of users 144 on a shared project, content input(s) 152 e / 252 e are likely to be relevant to that collaboration. However, the content provided by or obtained from news wires 150 a , social media platforms 150 b , video source 150 c , and/or crowd source 150 d may or may not be relevant to the collaborative project of users 144 . Thus, news feed content input 152 a / 252 a may include information on a variety of different subjects other than the subject matter on which users 144 are collaborating. By analogy, each of social media content input 152 b / 252 b , video content input 152 c / 252 c , and crowd sourced content input 152 d / 252 d may be relevant to a variety of different subjects other than the collaborative subject matter.

Flowchart 360 continues with parsing each of content inputs 452 for metadata describing the content input (action 362 ). Referring specifically to exemplary content input 452 n included among content inputs 452 , content input 452 n may include metadata 472 describing content input 452 n . Metadata 472 may include a time stamp and/or date stamp corresponding to generation of content input 452 n , as well as one or more keywords or other data describing content input 452 n . In addition, or alternatively, metadata 472 may identify the source of content input 452 n . Parsing of content input 452 n for metadata 472 describing content input 452 n may be performed by content classification software code 120 / 220 / 420 , executed by hardware processor 104 / 204 , and using metadata parsing module 422 .

As noted above, one advantage conferred by content processing system 100 is that journalist users among users 144 , including editors, can quickly assess what's reportable and what isn't. In some implementations, that assessment may be made based on metadata 472 describing content input 452 n . For example, metadata 472 may identify organizations and/or news outlets having rights to broadcast or otherwise publish content input 452 n . Moreover, in instances in which rights to the publication of content input 452 n are held for some communication channels but not others, metadata 472 may include that additional information. As a specific example, where the organization represented by users 144 has television broadcast rights to content input 452 n but lacks the right to publish content input 452 n online via the Internet, metadata 472 may include that distinction.

Flowchart 360 continues with, for each content input 452 n , obtaining description 476 of language-based content 474 included in content input 452 n from natural language processor 114 / 214 / 414 (action 363 ). Natural language processor 114 / 214 / 414 may include ANN 116 / 216 , for example, trained to interpret language-based content 474 . Description 476 of language-based content 474 included in content input 452 n may be obtained from natural language processor 114 / 214 / 414 by content classification software code 120 / 220 / 420 , executed by hardware processor 104 / 204 , and using content analysis module 424 .

Flowchart 360 continues with, for each content input 452 n , obtaining description 480 of visual content 478 included in content input 452 n from computer vision analyzer 118 / 218 / 418 (action 364 ). Computer vision analyzer 118 / 218 / 418 may be configured to assess and interpret visual content 478 included in content input 452 n . Description 480 of visual content 478 included in content input 452 n may be obtained from computer vision analyzer 118 / 218 / 418 by content classification software code 120 / 220 / 420 , executed by hardware processor 104 / 204 , and using content analysis module 424 .

Flowchart 360 continues with, for each content input 452 n , associating predetermined annotation tags 482 with content input 452 n based on metadata 472 describing content input 452 n , description 476 of language-based content 474 included in content input 452 n , and description 480 of visual content 478 included in content input 452 n (action 365 ). In one implementation, predetermined annotation tags 482 may be selected from a finite and well defined set of annotation tags utilized to classify content input 452 n for storage in reference database 112 / 212 .

For example, predetermined annotation tags 482 may identify content input 452 n by a content designation such as news or entertainment, or may identify or otherwise characterize persons, objects and/or locations shown or described by content input 452 n . Association of predetermined annotation tags 482 with content input 452 n based on metadata 472 , description 476 of language-based content 474 , and description 480 of visual content 478 may be performed by content classification software code 120 / 220 / 420 , executed by hardware processor 104 / 204 , and using annotation module 426 .

Flowchart 360 may conclude with, for each content input 452 n , assigning content input 452 n to a respective predetermined subject matter classification 484 based on predetermined annotation tags 482 associated with content input 452 n (action 366 ). Assignment of content input 452 n to predetermined subject matter classification 484 based on predetermined annotation tags 482 maybe performed by content classification software code 120 / 220 / 420 , executed by hardware processor 104 / 204 , and using classification module 428 .

In use cases in which users 144 are journalists and/or expert analysts collaborating on a news story, that news story may serve as subject matter classification 484 , or subject matter classification 484 may otherwise correspond to the news story. Thus, in such a use case, all relevant content inputs to news story/subject matter classification 484 from all content sources can be advantageously classified together by automated content processing system 100 / 200 , may be stored in reference database 112 / 212 , and may thereafter be accessed and/or updated by users 144 .

For example, where subject matter classification 484 corresponds to a news story, all news content 152 a , social media content 152 b , video content 152 c , crowd sourced content 152 d , and content input(s) 152 e / 252 e submitted by users 144 and relevant to the news story can be segregated from content inputs that are not relevant, and may be stored together. Consequently, content processing system 100 / 200 enables collaborating users 144 to access all information 154 / 254 relevant to their collaborative project concurrently, from a single source, i.e., reference database 112 / 212 .

It is noted that in the exemplary use case in which subject matter classification 484 corresponds to a news story or other collaborative project of user 144 , each of users 144 a and 144 b may be authorized to access predetermined subject matter classification 484 . Moreover, where users 144 are journalists collaborating on a news story, content input(s) 152 e / 252 e submitted by one or both of users 144 a and 144 b may be journalistic contributions to the news story being worked on in collaboration.

In some implementations, content classification software code 120 / 220 / 420 , when executed by hardware processor 104 / 204 , may be configured to perform machine learning to further enhance its content classification performance. For example, in one implementation, hardware processor 104 / 204 may execute content classification software code 120 / 220 / 420 to compare content input 452 n to other content inputs assigned to the same predetermined subject matter classification 484 , and to generate one or more new annotation tags and/or other descriptive metadata based on the comparison, in an automated annotation process.

That is to say, in addition to associating predetermined annotation tags 482 with content input 452 n , in some implementations, content classification code 120 / 220 / 420 may also generate new annotation tags and may associate the new annotation tags with content input 452 n and/or other content inputs commonly assigned to subject matter classification 484 . Consequently, content classification code 120 / 220 / 420 can track trends and identify connections between content inputs 452 to content processing system 100 / 200 that would not be trackable or identifiable based on information carried by content inputs 452 alone.

Thus, the present application discloses content processing systems and methods configured to provide a single collaborative tool for use across an entire organization. The content processing solution disclosed in the present application can be used to substantially minimize the time for an organization to ingest, analyze, and distribute content, while concurrently enhancing the richness of that content. When a system user receives or generates information about a collaborative project, the user inputs the information to the content processing system where it can become assigned with that specific collaborative project in an automated process. Thereafter, all other members of the collaboration team can advantageously view, comment on, or further update the content assigned to the collaboration.

From the above description it is manifest that various techniques can be used for implementing the concepts described in the present application without departing from the scope of those concepts. Moreover, while the concepts have been described with specific reference to certain implementations, a person of ordinary skill in the art would recognize that changes can be made in form and detail without departing from the scope of those concepts. As such, the described implementations are to be considered in all respects as illustrative and not restrictive. It should also be understood that the present application is not limited to the particular implementations described herein, but many rearrangements, modifications, and substitutions are possible without departing from the scope of the present disclosure.