Patent Publication Number: US-11395051-B2

Title: Video content relationship mapping

Description:
BACKGROUND 
     The present disclosure relates to enhanced digital video content mapping using a data descriptor, such as a content tag. More specifically, the present disclosure relates to enhanced digital video content mapping using a cognitive analysis to tag content based on data extracted from the digital video content. 
     Devices and services can be used for a user to view digital video content. Such devices and services can include video recorders, digital downloads, streaming devices, or peer to peer file sharing to select digital video content. Because of the ease of access to digital video content, viewers are often presented with a plethora of digital video content to view, for example, as provided by a provider of movies, television shows (e.g., cable television), or other content service providers with various formats and services providing audio and visual content. As viewing digital video content is prolific, users can spend a significant amount of time programming and selecting content to view, and users can use many techniques for flagging or selecting digital video content. 
     In one scenario, a user can choose and view digital video content using a digital video content viewer. The content viewer may use an algorithm to gather additional digital video content for the viewer to watch. The content viewer may also arbitrarily present additional digital video content to the user. However, it can be difficult to determine the relevance of suggested digital video content based on a suggestion method that does not account for a user&#39;s viewing preferences. In either case, the additional digital video content suggestions may be made without regard for the user&#39;s viewing preferences. In one example, the user may not know until minutes into watching an instance of suggested digital video content whether it is based on the user&#39;s viewing preferences. For a user, the process of finding additional relevant digital video content to watch can be a process of trial and error, which is a cumbersome technique to select content to view. 
     This can result in viewers spending a significant amount of time watching irrelevant digital video content and also spending a significant amount of time viewing unwanted content. 
     SUMMARY 
     The present disclosure recognizes the inefficiencies of present digital content viewing and selection, thus, what is needed is a process and system for identifying digital content, which can be used for determining relevant digital video content for a viewer or user. The present invention can identify and determine similar digital video content, and indicate the strength of similarities between instances of digital video content. 
     The present invention provides a method for digital video content mapping including receiving and parsing a plurality of instances of digital video content using a computer. The digital video content includes audio and/or visual data available over a content delivery system communicating with the computer. The method defines content tokens for tagging content in the digital video content. The defining of the content tokens is based on a cognitive analysis of the digital video content. Related content tokens are identified in the plurality of digital video content using the cognitive analysis, and the identified related content tokens are tagged. A relationship map between the tagged related content tokens is created using the cognitive analysis. The relationship map indicates a strength of a relationship between the tagged content tokens and thereby the plurality of instances of digital video content. 
     In another embodiment according to the present invention, a system for digital video content mapping is presented. The system comprises a computer system comprising: a computer processor, a computer-readable storage medium, and program instructions stored on the computer-readable storage medium being executable by the processor, to cause the computer system to perform a method, the method including receiving and parsing a plurality of instances of digital video content using a computer. The digital video content includes audio and/or visual data available over a content delivery system communicating with the computer. The system defines content tokens for tagging content in the digital video content. The defining of the content tokens is based on a cognitive analysis of the digital video content. Related content tokens are identified in the plurality of digital video content using the cognitive analysis, and the identified related content tokens are tagged. A relationship map between the tagged related content tokens is created using the cognitive analysis. The relationship map indicates a strength of a relationship between the tagged content tokens and thereby the plurality of instances of digital video content. 
     In another embodiment according to the present invention, a computer program product in a computer networked environment for digital video content mapping is presented. The computer program product comprises a computer readable storage medium having program instructions embodied therewith. The computer readable storage medium is not a transitory signal per se, and the program instructions are executable by a computer to cause the computer to perform a method, the method including receiving and parsing a plurality of instances of digital video content using a computer. The digital video content includes audio and/or visual data available over a content delivery system communicating with the computer. The computer program product defines content tokens for tagging content in the digital video content. The defining of the content tokens is based on a cognitive analysis of the digital video content. Related content tokens are identified in the plurality of digital video content using the cognitive analysis, and the identified related content tokens are tagged. A relationship map between the tagged related content tokens is created using the cognitive analysis. The relationship map indicates a strength of a relationship between the tagged content tokens and thereby the plurality of instances of digital video content. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity in facilitating one skilled in the art in understanding the invention in conjunction with the detailed description. The drawings are discussed forthwith below. 
         FIG. 1  is a schematic block diagram illustrating an overview of a system and methodology for using cognitive analysis to create a relationship map of digital video content for a viewer. 
         FIG. 2  is a schematic block diagram depicting a computer system according to an embodiment of the disclosure which includes a detailed depiction of a service computer (shown generally in  FIG. 1 ) which cooperates with the system and methods shown in  FIG. 1 . 
         FIG. 3  is a flow chart depicting a method according to embodiments of the present invention. 
         FIG. 4  is a schematic block diagram depicting a content extraction engine and the tagging of content tokens according to an embodiment of the present invention. 
         FIG. 5  is a schematic block diagram depicting a relationship mapping engine and the creation of a relationship map according to an embodiment of the present invention. 
         FIG. 6  is a diagram depicting the mapping of tags and the creation of a relationship map according to an embodiment of the present invention. 
         FIG. 7  is a schematic block diagram depicting a computer system according to an embodiment of the disclosure which includes a detailed depiction of a computer (shown generally in  FIG. 1 ) and which cooperates with the system and methods shown in  FIGS. 1, 2, 3, 4, and 5 . 
         FIG. 8  depicts a cloud computing environment according to an embodiment of the present invention. 
         FIG. 9  depicts abstraction model layers according to an embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention as defined by the claims and their equivalents. It includes various specific details to assist in that understanding but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness. 
     The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention is provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents. 
     It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces unless the context clearly dictates otherwise. 
     Referring to  FIGS. 1, 2, and 3 , in one embodiment of the present disclosure, a system  15  and a method  300  are shown for video content relationship mapping. In one embodiment, a user(s) or an initiating user  110  can use an initiating computer, embodied as a user computer  190 , to receive and view digital video content  134  delivered by a content delivery system  128 . The user computer  190  can be any number of computing devices, for example, a mobile device, tablet, desktop or laptop computer connected to a graphical user interface (GUI)  192 . The (GUI)  192  can be any user interface that allows users to interact with electronic devices (such as the user computer  190 ) through graphical icons. In another embodiment, the user  110  can view the digital video content  134  using an auditory and video receiving device embodied as a viewing screen  113 , e.g., a television screen, connected to a set-top box  115 . The set-top box  115  includes devices capable of converting digital television signals for viewing, for example, a cable box. The user  110  can use the viewing screen  113  to view instances of the digital video content  134  delivered by the content delivery system  128 . 
     The content delivery system  128  can be used to deliver instances of digital video content  134  to devices across the communications network  122 . The content delivery system  128  can contain a streaming module  206 . The streaming module  206  can transmit instances of digital video content  134  over the Internet  122  to the service computer  112 . The content delivery system  128  can communicate with the previously mentioned service computer  112  via the Internet  122 . The Internet  122  connection can be managed by the service computer  112 . The devices capable of receiving a number of instances of the digital video content  134  can include, for example, digital video recorders, mobile devices, and set-top cable boxes. The content delivery system  128  can include, for example, cable services, satellite systems, streaming services, and any current or future digital video content delivery device, service or system. The digital video content  134  can include audio and/or visual data being available over the content delivery system  128  communicating with the service computer  112 . 
     A service computer  112  can interact with the content delivery system or service  128  using a communications network, e.g., the Internet  122 , to receive and perform a cognitive analysis  111  on instances of digital video content  134  in order to extract content tokens  104  and build a relationship map  114  which may help determine the relevance of the instance to the user  110 . The service computer  112 , may be a server, for example, a back-end server or remote server at least from the perspective of the user. 
     In further embodiments according to the present disclosure, the components and function of the service computer  112  can be incorporated into the user computer  190 . In another embodiment, the components and function of the service computer can be incorporated into a computer or a content service provider computer residing locally. 
     The service computer  112  includes a content extraction engine  108  for the extraction of data, for example, the data can include: audio data, visual data, and metadata from instances of received digital video content  106 . Metadata is data that can provide additional information about other data. One type of metadata is descriptive metadata. Descriptive metadata can describe a resource for identification purposes. Descriptive metadata can include elements such as abstracts, authors, keywords, and titles. In embodiments of the present invention, descriptive metadata can describe the contents of digital video content  134 , which can include specific actors, character personalities, genres, and themes. The content extraction engine  108  can generate content tokens  104  based on an extraction of the descriptive data from digital video content  134 , as will be explained below. 
     The extracted data is used to generate content tokens  104  which may describe the audio and/or visual aspects of a particular instance of the received digital video content  106 . The content tokens  104  may be defined based on a content definition criteria. The content definition criteria, and thereby the content tokens, is based on the data. 
     Such content can include a plurality of aspects of the content, such as, a particular person as part of the content, such as an actor, or a place or thing that appears in the content. Also, the content definition criteria can include a type of content such as a genre, theme, or subject. Further, the extracted data can include key information such as interpersonal relationships between characters, character affinities (likes/dislikes), plot depth of a show, and so on. This data would otherwise be unknown to a user  110  based upon a cursory review of a show description provided by a TV guide or information guide on a streaming service. 
     Such types of content and the content tokens can be sorted based on the frequency of occurrence. Thereby, the content definition criteria enables the identification of content specific to the user&#39;s viewing history and thereby the user. The content definition criteria can include one or more aspects of the content that enable comparison of a plurality of digital video content to ascertain similarity of the content including portions of the content for mapping the relationship (e.g., similarity or relevance) between the content of the plurality of the digital video content as is explained further in the present disclosure. 
     In one example, a cognitive analysis  111  can define the content using cognitive computing techniques. Cognitive computing is the simulation of human thought processes in a computerized model. Cognitive computing involves self-learning systems that use data mining (e.g., parsing content for relevant data), pattern recognition (e.g., detecting patterns in content based on specified criteria), and natural language processing (e.g., interpreting human language to find patterns) to mimic the way the human brain works. Embodiments of the present invention can self-learn, data mine, recognize patterns and employ natural language processing techniques to define content for identification (using the content definition criteria), and thereby generate the content tokens  104 . 
     Additionally, content definition can be based on content descriptors, which can include the extracted metadata describing the received digital video content  106 . The metadata may be placed in the received digital video content  106  by a content provider. The content extraction engine  108  extracts the metadata as well as audio and visual data and creates content tokens  104  from the instance of received digital video content  106  using the cognitive analysis  111  ( FIG. 2 ). 
     The cognitive analysis  111  can include a number of application program interfaces (APIs) (shown in  FIG. 2 ) in order to define the metadata, audio, and/or visual data in the received digital video content  106 . The APIs can be computer code that allows two software programs to communicate with each other, for example, a visual recognition API and a speech transcription API can detect and label the audio and visual information contained in the digital video content  134  as will be described below. The cognitive analysis  111  can use an audio recognition module  214  and a visual recognition module  218  to analyze scenes in the received digital video content  106  for objects, faces, text and other content. The audio recognition module  214  (shown in  FIG. 2 ) can transcribe audio information in the received digital video content  106  into the content tokens  104  as audio is identified by the cognitive analysis  111 . The cognitive analysis  111  can use the visual recognition module  218  (shown in  FIG. 2 ) to generate content tokens  104  describing the images in the received digital video content  106  by actors, objects, scenery and other key features based on the cognitive analysis  111 . 
     The service computer  112  also includes a relationship mapping engine  118 . The relationship mapping engine  118  can create a relationship map  114  between instances of digital video content  134 . The relationship map  114  is created based on a determined strength of the relationship between the instances of digital video content  134 . The strength of the relationship is used to predict the likelihood that the user  110  would prefer specific digital video content  134 . The strength of the relationship in the relationship map  114  can be based on a low or high frequency of tagged content tokens in the digital video content. 
     The content extraction engine  108  and a relationship mapping engine  118 , work in tandem to analyze digital video content  134  and build the relationship map  114  (shown in  FIGS. 2 and 6 ) between the digital video content  134  as will be described in more detail below. In one example, the digital video content  134  is streamed to the service computer  112 . The digital video content  134  is referred to as received digital video content  106  upon delivery to the service computer  112 . The cognitive analysis  111  can parse the received digital video content  106  for content descriptors and label the content descriptors as content tokens  104  using a number of APIs. A visual recognition API, for example, a visual recognition module  218  (shown in  FIG. 2 ) can recognize visual data in the (received digital video content  106 ) and an audio recognition module  214  (shown in  FIG. 2 ) can recognize audio in the received digital video content  106  which is transformed into text. A natural language procession module  216  may analyze the transformed text to extract information such as character traits, interactions or relationships between characters, plot depth, etc. The audio and visual data may be defined as a number of content tokens  104  for tagging content. The cognitive analysis  111  can use a tag generation module  220  to create tags  102  for tagging the content tokens  104  and tag the content tokens  104  as they are identified in the digital video content. 
     The content tokens  104  indicate or describe portions of the digital video content  134 . As will be described below in more detail, embodiments of the present invention can analyze data in instances of digital video content  134  in order to identify content for application of content tokens  104  in instances of digital video content  134 . The content tokens  104  can capture the data in the digital video content  134  and label the themes or tones therein, in order to categorize the audio/visual data, for example, by actors, type, and theme. The content tokens  104  are used for tagging the received digital video content  106  where the content tokens  104  appear when digital video content matches the content definition criteria. The tagged content tokens are used to generate a relationship map  114  (shown in  FIG. 2 ) depicting a strength of a relationship between instances of digital video content  134 , as will be described below. 
     The content tokens  104  are identified by the cognitive analysis  111  (shown in  FIG. 2 ) in order to determine the contents of the digital video content  134 . A number of tags  102  are created by the cognitive analysis  111 . The tags  102  are created for use as markers of where specific content tokens  104  appear in the received digital video content  106 . Related content tokens  104  may be identified by the cognitive analysis  111  between instances of digital video content  134  (which can be considered related tags  117 ). After the tags  102  are identified in the digital video content  134 , the digital video content  134  is referred to as tagged content  119  (shown in  FIG. 2 ). Once the tags  102  are created for the digital video content  134 , related tags  117  between instances of tagged content  119  can be used to build a relationship map  114  using the cognitive analysis  111 . The relationship map  114  may indicate a strength of a relationship between the tags  102  with respect to instances of digital video content  134  as will be further explained in the embodiments of the invention. 
     It is understood that the service computer  112 , the user computer  190 , and the computer system  1010  are representative or illustrative of many alternative computer devices and are presented here as generic representations for the purposes of the embodiment(s) of the present invention. In the embodiment discussed below, for illustrative purposes, the method of the present disclosure can be embodied in the program  1060  ( FIG. 7 ) or a software initiated from a client computer such as the computer system  1010 . The computer system  1010  is discussed in more detail with reference to  FIG. 7 . 
       FIG. 2  depicts a detailed view of the service computer  112  depicted in  FIG. 1 . The service computer may include a content extraction engine  108 , a relationship mapping engine  118  and a datastore  120 . 
     The content extraction engine  108  can contain APIs capable of performing functions such as audio recognition (using an audio recognition module  214 ), natural language processing (using a natural language processing (NLP) module  216 ), and visual recognition (using a visual recognition module  218 ). The content extraction engine  108  may also contain a tag generation module  220 . The tag generation module  220  can automatically define tags  102  for instances of digital video content  134  using the service computer  112  APIs communicating with the content delivery system  128  (shown in  FIG. 1 ). The tag storage module  215  can store the tags  102  for use by the relationship mapping module  213 . 
     The relationship mapping engine  118  can contain a relationship mapping module  213  and related tags  117 . The relationship mapping engine  118  can use the relationship mapping module  213  to generate a relationship map  114  between one or more instances of tagged digital video content  134 . The relationship mapping module  213  can store the relationship map  114  in the data store  120  on the service computer  112 . The relationship mapping engine may compare tags  102  between instances of digital video content  134  to determine related tags  117  which are used to determine the strength of relationship between the instances of digital video content  134  in the relationship map  114 . The tag storage module  215  can store the related tags  117  for use by the relationship mapping module  213 . Related tags  117  may alternatively be discarded once the digital video content has been added to the relationship map  114  to minimize storage space. 
     The data store  120  can include the tags  102 , the related tags  117 , the relationship map  114 , and tagged content  119 . As discussed above, the tags  102  tag content tokens (also referred to as tagged content tokens) marking data. The related tags  117  are tags that are related to other tags (and thereby related to other content tokens) in other instances of received digital video content  106 . The tagged content  119  represents instances of the received digital video content  106  that have been tagged using the tags  102 . Tagged content  119  may alternatively be discarded once the digital video content has been added to the relationship map  114  to minimize storage space wherein the user  110  would extract data from the service computer  112 , then receive the desired content from the content delivery system  128 . 
       FIG. 3  depicts a method  300  which can perform the tagging of content tokens  104  based on the recognition of content according to an embodiment of the present invention. The elements depicted in  FIGS. 1 and 2  can be used to perform the method  300 . At block  302 , the method includes receiving and parsing an instance of digital video content  134  using a computer, which in this example can be a service computer  112 . The service computer  112  can receive the digital video content  134  and input it into the content extraction engine  108 . The digital video content  134  is streamed over the communications network, e.g., the Internet  122 , using the content delivery system  128 . Upon receipt by the service computer  112 , the digital video content  134  is referred to as a received digital video content  106 . The received digital video content  106  is parsed by the content extraction engine  108  performing a cognitive analysis  111  of the received digital video content  106  using a number of APIs. For example, the method can use the cognitive analysis  111  to perform functions such as visual recognition for video and speech to text translation for audio in order to interpret the contents of the received digital video content  106 . The cognitive analysis  111  can use can use a number of APIs to perform the visual recognition and speech to text translation. 
     The method can define a number of content tokens  104  for tagging content in the digital video content at block  304 . The content tokens  104  may be defined based on a content definition criteria, which is based on content descriptors included in the digital video content  134  by the content provider. The content descriptors, can include metadata describing the received digital video content  106 . The metadata may be placed in the received digital video content  106  by a content provider as a number of the content tokens  104  when the digital video content  134  is placed on the content delivery system  128 . The method can define the audio and/or visual data in the received digital video content  106  after the parsing is performed by the content extraction engine  108  at block  302 . For example, the method can use the audio recognition module  214  and the visual recognition module  218  to analyze scenes in the received digital video content  106  for objects, faces, text and other content. The audio recognition module  214  can transcribe audio information in the received digital video content  106  into content tokens  104  as speech is heard. The visual recognition module  218  can generate content tokens  104  describing the images in the received digital video content  106  by actors, objects, scenery and other key features based on the cognitive analysis  111 . 
     At block  306  a number of content tokens  104  can be identified in the received digital video content  106 . A content extraction engine  108  ( FIG. 4 ) can be used to identify content and thereby apply content tokens by extracting the data included in the digital video content  134  and using the information to create content tokens  104  from the received digital video content  106 . The output of the cognitive analysis  111  can be a number of corresponding content tokens  104  describing the audio and/or visual elements of the received digital video content  106 . 
     At block  308 , the identified content tokens  104  are tagged as in they occur in the instance of received digital video content  106 , creating tags  102  ( FIGS. 2 and 4 ). The content extraction engine  108  can use the tag generation module  220  to tag where the content tokens  104 , defined at block  304 , occur in the received digital video content  106 . The tagged content tokens are represented as tags  102 . The tags  102  can include descriptive information, or a time stamp, such as a length and a duration of occurrence of the content tokens  104  in the received digital video content  106 . The tagging process will be explained in greater detail with the explanation of  FIG. 4  below. 
     At block  310 , the method identifies a frequency of tags  102  occurring in the received instances of digital video content  106 . The frequency of the tags  102  can be determined by several methods, for example, by tallying the number of occurrences of the tags  102 . 
     At block  312 , the method can identify related content tokens  104  in other instances of digital video content  134  based on the tags  102  of the first instance of received digital video content  106 . The method can compare tags  102  from a second received digital video content  106  for matching the tags  102  from the first received digital video content  106  that were created at block  308 . The method can tag the related content tokens with tags (for example, the tagged related content tokens can be referred to as related tags  117 ). The tagging is accomplished using the tag generation module  220 . 
     The method includes designating a strength of the relationship between related tagged content as identified in instances of the digital video content  134 , at block  314 . The method can designate a high strength of the relationship between the instances in response to a high frequency of related tags  117 . The method can also designate a low strength of the relationship between the identified instances in response to a low frequency of related tags  117 . 
     The method can use the relationship mapping engine  118  (shown in  FIG. 1 ) to map instances of the digital video content  134  with the identified high or low strength of the relationship at block  316 . When instances of the digital video content  134  are mapped together, the mapping indicates that the instances likely contain similar content. The relationship mapping process is described in detail below in  FIGS. 5 and 6 . The strength of the relationship between instances of digital video content may be rated on a number of categories (e.g., two instances of digital video content may have a high strength of relationship in that they are both related to law enforcement, but they may also have a low strength of relationship in that one is a drama with a deep plot and the other is a comedy. 
     The method  300  includes creating and updating the relationship map  114  using the strength of relationship between instances of digital video content at block  318 . The relationship mapping engine  118  (shown in  FIG. 2 ) creates the relationship map  114  (shown in  FIG. 1 ) to reflect a determination of a strength of a relationship using the tags  102  and the related tags  117 . The relationship mapping engine  118  can use the tag storage module  215  ( FIG. 2 ) to store the tags  102 , and the relationship map  114 . The relationship mapping module  213  can associate instances of digital video content  134  with each other based on the strength of the relationship between the tags  102  contained in the instances of digital video content  134 . The relationship map  114  can be built between shows, seasons, series, etc. based on the number of related tags  117  (that is, tags that are tagging similar content tokens) tagging instances of received digital video content  106 . At block  320 , the method can store the created relationship map  114  on a datastore  120  residing on the service computer  112 . 
     At block  322 , the method can determine whether the content provider has added new instances of digital video content  134 . If the method determines that the content provider has added new instances of digital video content  134 , the method returns to block  302  and performs the method steps again including the cognitive analysis  111 . The updating of the relationship map at block  318  can be performed by tags  102  being applied as a result of a cognitive analysis  111  of the received digital video content  106 . The relationship mapping process is further described below with reference to  FIGS. 5 and 6 . When the method  300  detects that no additional instances of digital video content  134  have been added, the method can end at block  324 . 
     Referring to  FIG. 4 , an embodiment of the method  400 , depicts a content extraction engine  108  and the generation of tags  102 . In this example, a content provider adds an instance of digital video content  134  to a content delivery system  128  using a computer. The digital video content is delivered to the user  110  using a content delivery system  128 . The content delivered to the service computer  112  (by the content delivery system  128 ), user computer  190 , and/or set-top box  115  as digital video content  134  and the received digital video content is depicted as digital video content  106 . It may contain audio and/or visual elements. The content extraction engine  108  can parse the received digital video content  106  in order to define content tokens  104  within the received digital video content  106 . The content tokens  104  can be audio and/or visual information describing the instance of digital video content  134 . The received digital video content  106  is input into the content extraction engine  108 . 
     The content extraction engine  108  can use a number of APIs (depicted as APIs  404 ) such as a visual recognition module  218  to detect visual information in the digital video content and label it as a content token  104 . An audio recognition module  214  can detect audio information and label it as a content token  104  as well. These recognized audio/visual/textual content tokens  104  are part of a cognitive analysis  111  that includes interpretation by NLP module  216  (shown in  FIG. 2 ) which may additionally analyze closed captioning data if available. The method may use the content tokens  104  for tagging (marking) the occurrence audio/visual/textual data in the digital video content. Tagged content  119  is representative of the received digital video content  106  that has been tagged. In this example, the tags  102  are placed in a data store  120  using a tag storage module  215  (shown in  FIG. 2 ) accessible by a relationship mapping engine  118  to build relationship map  114 . The content delivery system  128  can deliver related content to a set-top box  115  or a user computer  190  based on information from relationship map  114 . 
     Thereby, in the method of the present disclosure, the content definition criteria (and thereby the content tokens) enables the identification of content specific to the user and the user&#39;s viewing history. This is in contrast to recommending content or works to a user based on genres (such as comedy, or drama), or other viewer&#39;s preferences who have viewed the same content as the user. In the present disclosure, the content definition criteria and thereby the content tokens, are used to tag content that appear in the body of work itself, thus recommendations to the user are specific to the content itself of the body of work. 
     Referring to  FIG. 5 , an embodiment of the method  500 , depicts a relationship mapping engine  118 . The relationship mapping engine  118  can use a number of APIs (represented as API(s)  404 ) from cognitive analysis  111  for cognitive matching of tags  102  to generate related tags  117 . In this example, tags  107 A for a first digital video content  106 A, and tags  107 B for a second digital video content  106 B are stored on a datastore  120  and used as inputs to the relationship mapping engine  118 . The relationship mapping engine  118  accesses the data store  120  and can compare the tags for the first digital video content  107 A with tags  102  of other instances of received digital video content  107 B to generate related tags  117 . The relationship mapping engine  118  can use a cognitive analysis  111  to determine a strength of the relationship between the instances. A relationship map  114  can be created between the first digital video content  106 A and the second digital video content  106 B based on the strength of the relationship between the instances of content. In one example, one factor that initiates a relationship map can include a match of high frequency tags  102 , e.g. a high strength of a relationship between the instances of digital video content. The strength of the relationship can be based on tag occurrence (expressed as a numerical tally), or a number of matching tags, or an NLP analysis that shows commonalities between characters, plot lines, etc. In either case, the strength of the relationship is used to evaluate specific instances of digital video with respect to each other. The relationship mapping engine  118  repeats this process for each content instance of digital video content. The relationship map  114  is continually updated as additional instances digital video content  134  complete the content extraction process ( FIG. 4 ). The relationship map  114  uses the strength of the relationship between instances of digital video content, for example, digital video content  106 A and digital video content  106 B (as shown in  FIG. 5 , and discussed in more detail below) to build a relationship map  114 . The content delivery system  128  can deliver relevant digital video content to users  110  based on suggestions from the relationship map  114 . 
     An embodiment of a relationship map  114 , according to an embodiment of the present invention, is shown in  FIG. 6 . The relationship map  114  is a visual depiction of the strength of the relationship between instances of digital video content. A plurality of instances of the digital video content are identified based on a content provider adding new digital video content to a content delivery system  128 . The plurality of identified instances are tagged where content tokens are marked in the digital video content. Here, boxes depicted as digital video content F  601 , digital video content G  602 , digital video content H  603 , digital video content I  604 , represent the instances of digital video content (also referred to as instances F, G, H, and I). Tags  1 ,  2 ,  3 ,  4 ,  5 ,  6 ,  7  depict tags which are created using the content extraction engine  108 . 
     In the example shown in  FIG. 6 , R NM  indicates a strength of the relationship between instances of digital video content. R can be representative of a number of frequency determination factors, for example, the sum of the frequency of the instances of the digital video content. “N” and “M” respectively are each an instance of digital video content. In this embodiment, the relationship between instances of digital video content can be determined by summing the product of the frequency of appearance of each tag within each instance of digital video content. In one example, the strength of the relationship between instance F  601  and instance G  602  can be computed using the equation R FG =f 1 *g 3 +f 2 *g 1 . f 1  is the frequency at which tag  1  appears in instance F  601 . g 3  is the frequency at which tag  1  appears in instance G  602 . f 1  is multiplied by g 3  to determine the number of occurrences of tag  1 . For example, f 1 =5 occurrences, and g 3 =3 occurrences, resulting in 3×5=a weighting factor of 15. Tag  1  is generated as an indication of the occurrences of tag  1  appearing in instance F  601  and instance G  602 . Instances F  601  and G  602  also both contain tag  3 . The frequency of tag  3 &#39;s occurrence in instance F  601  is depicted as f 2 . Similarly, the frequency of the occurrence of tag  3  in instance G  602  is depicted as g 1 . In this example, f 2 =2 and g 1 =5 for a weighting factor of 10 (2×5=10). Tag  3  is generated as an indication of the occurrences of tag  3  in instance F  601  and G  602 . Summing the products of the frequencies of occurrences of the content tokens results in 10+15=25 for a total summed weighting factor of 25. 
     Summing these products, (which depict the frequency of each tag within each instance of digital video content), provides a mathematical depiction of the strength of the relationship between instances of digital video content. The relationship mapping engine  118  can use the aforementioned mathematical equations to create a relationship map  114  between instances of received digital video content  106 . The relationship map can be updated based on the relationship strength of the tagged instances. The numerical tally of the number of occurrences of the tags in the identified content instances is correlated with a strength of the relationship between the identified content instances. The greater the frequency of the occurrence of common tags, the stronger the relationship between content instances of the digital video content. In one example, advertisers could use the determined strength between instances of the digital video content in order to make marketing decisions about a target audience. For example, the advertisers could determine that viewers prefer specific instances of digital video content and place advertisements in the instances in order to increase the likelihood that the advertisements will be viewed by the viewers. 
     In one example, an advantage of the present invention is that it provides a process for analyzing digital video content and forming a relationship map between media based on the content extracted from the digital video content. The digital video content is analyzed to determine when and where specific tags appear within the digital video content. Once created, the tags are sorted by frequency of occurrence. These tags can include tone, theme, personality of characters, etc. and can be used to relate instances of digital video content. The present invention builds a relationship map between digital video content based on the tagging of the content. Another advantage of the present disclosure is that it includes a method of tagging digital video content without requiring user input. 
     In one embodiment of the present invention, digital video content is parsed via a content extraction engine which includes an interface. An interface can include, for example, an application program interface (API). An API is a set of routines, protocols, and tools for building software applications. An API specifies how software components should interact with each other to achieve a desired result or output. According to embodiments of the present invention, APIs are programmed to interpret digital video content and determine the strength of the relationship between instances of digital video content. 
     Watson™ Embodiment 
     One embodiment of the present disclosure can use IBM Watson® including Watson™ APIs (herein referred to as the Watson™ embodiment). In one example of the present disclosure, a content extraction engine can include but is not limited to Watson™ APIs. Watson™ APIs can be used to extract content tags from digital video content. The digital video content can be analyzed to determine when and where specific tags appear within the body of the digital video content. 
     The Watson™ embodiment of the present disclosure may use Watson™ APIs to achieve the desired result, i.e., a relationship map that determines the strength of relationship between instances of video content. Watson™ APIs enable cognitive computing features in computer applications. 
     Continuing with the Watson™ embodiment of the present disclosure, the embodiment can include a cognitive text analyzer, such as Watson™ AlchemyLanguage service. Watson™ AlchemyLanguage is a collection of text analysis functions that derive semantic information from inputted digital video content. Extracted text which could be hypertext markup language (HTML), or a public universal resource locator (URL) may leverage sophisticated natural language processing techniques to get a quick high-level understanding of the inputted digital video content and obtain detailed insights such as directional sentiment from entity to object. Natural language processing is a field of computational linguistics concerning the use of human language, specifically programming computers to process natural language text and understand human speech as it is spoken. In one embodiment of the present invention, a content extraction engine can use the Watson™ AlchemyLanguage API to generate tags from digital video content. 
     The Watson™ embodiment of the present disclosure can include a visual recognition API, such as Watson™ Visual Recognition API. Watson™ Visual Recognition API uses deep learning algorithms to analyze images for scenes, objects, faces and other content. The response includes keywords that provide information about the content. A set of built-in classes provides highly accurate results without a user training the API. In one embodiment of the present invention, a content extraction engine can use the Watson™ Visual Recognition API to generate tags from digital video content. 
     The Watson™ embodiment of the present disclosure can include a speech transcription service, such as Watson™ Speech to Text API. A speech transcription service converts an audio file into a textual record using software that recognizes auditory messages and associates them with related text. The Watson™ Speech to Text service provides an API that adds speech transcription capabilities to the invention. The service transcribes the human voice using machine intelligence to combine information about grammar and language structure with knowledge of the composition of the audio signal. The service continuously returns and retroactively updates the transcription as more speech is heard. The resulting output can be used to generate tags from digital video content. 
     The Watson™ embodiment of the present disclosure can include a human speech tone analyzer, such as Watson™ Tone Analyzer. In conjunction with the Watson™ Speech to Text Transcription Service, the Watson™ Tone Analyzer provides a linguistic analysis of the communication tones in written text. The service allows the user to understand conversations and communications and respond appropriately. In this embodiment of the present invention, the Watson™ Tone Analyzer and Watson™ Speech to Text Transcription services can work in tandem to transcribe speech to text and provide auditory and written cues to generate tags from digital video content. 
     The Watson™ embodiment of the present disclosure can include a natural language classifier, such as the Watson Natural Language Classifier (NLC) service. The Watson™ Natural Language Classifier understands the intent behind text and returns a corresponding classification, complete with a confidence score. For example “what is the weather like today?” is a way of asking about temperature. The Natural Language Classifier can perform functions including categorizing volumes of written content. In this embodiment of the present invention, the NLC can categorize the transcribed text of audio extracted from received digital video content. 
     The present disclosure also provides an advantage to advertisers. Advertisers can use a relationship map to determine similar video content and target said content to increase the likelihood that targeted users will watch their advertisement. If the users desire to watch the content containing advertisements, there is a greater likelihood that the advertisement itself will be viewed. 
     Referring to  FIG. 7 , a system  1000  includes a computer system or computer  1010  shown in the form of a generic computing device. The method  300 , for example, may be embodied in a program(s)  1060  ( FIG. 7 ) embodied on a computer readable storage device, for example, generally referred to as memory  1030  and more specifically, computer readable storage medium  1050  as shown in  FIG. 7 . For example, memory  1030  can include storage media  1034  such as RAM (Random Access Memory) or ROM (Read Only Memory), and cache memory  1038 . The program  1060  is executable by the processing unit or processor  1020  of the computer system  1010  (to execute program steps, code, or program code). Additional data storage may also be embodied as a database  1110  which can include data  1114 . The computer system  1010  and the program  1060  shown in  FIG. 7  are generic representations of a computer and program that may be local to a user, or provided as a remote service (for example, as a cloud based service), and may be provided in further examples, using a website accessible using the communications network  1200  (e.g. interacting with a network, the Internet, or cloud services). It is understood that the computer system  1010  also generically represents herein a computer device or a computer included in a device, such as a laptop or desktop computer, etc., or one or more servers, alone or as part of a datacenter. The computer system can include a network adapter/interface  1026 , and an input/output (I/O) interface(s)  1022 . The I/O interface  1022  allows for input and output of data with an external device  1074  that may be connected to the computer system. The network adapter/interface  1026  may provide communications between the computer system a network generically shown as the communications network  1200 . 
     The computer  1010  may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types. The method steps and system components and techniques may be embodied in modules of the program  1060  for performing the tasks of each of the steps of the method and system. The modules are generically represented in  FIG. 7  as program modules  1064 . The program  1060  and program modules  1064  can execute specific steps, routines, sub-routines, instructions or code, of the program. 
     The method of the present disclosure can be run locally on a device such as a mobile device, or can be run as a service, for instance, on the server  1100  which may be remote and can be accessed using the communications network  1200 . The program or executable instructions may also be offered by a service provider. The computer  1010  may be practiced in a distributed cloud computing environment where tasks are performed by remote processing devices that are linked through a communications network  1200 . In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices. 
     More specifically, as shown in  FIG. 7 , the system  1000  includes the computer system shown in the form of a general purpose computing device with illustrative periphery devices. The components of the computer system  1010  may include, but are not limited to, one or more processors or processing units  1020 , a system memory  1030 , and a bus  1014  that couples various system components including system memory  1030  to processor  1020 . 
     The bus  1014  represents 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 limitation, 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. 
     The computer  1010  can include a variety of computer readable media. Such media may be any available media that is accessible by the computer  1010  (e.g., computer system, or server), and can include both volatile and non-volatile media, as well as, removable and non-removable media. Computer memory  1030  can include additional computer readable media  1034  in the form of volatile memory, such as random access memory (RAM), and or/cache memory  1038 . The computer  1010  may further include other removable/non-removable, volatile/non-volatile computer storage media, in one example, portable computer readable storage media  1072 . In one embodiment, the computer readable storage medium  1050  can be provided for reading from and writing to a non-removable, non-volatile magnetic media. The computer readable storage medium  1050  can be embodied, for example, as a hard drive. Additional memory and data storage can be provided, for example, as the storage system  1110  (e.g., a database) for storing data  1114  and communicating with the processing unit  1020 . The database can be stored on or be part of a server  1100 . Although not shown, a magnetic disk drive for reading from and writing to a removable, non-volatile magnetic disk (e.g., a “floppy disk”), and a an optical disk drive for reading from or writing to a removable, non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media can be provided. In such instances, each can be connected to bus  1014  by one or more data media interfaces. As will be further depicted and described below, memory  1030  may include at least one program product which can include one or more program modules that are configured to carry out the functions of embodiments of the present invention. 
     The method  300  FIG. 3 , for example, may be embodied in one or more computer programs, generically referred to as a program(s)  1060  and can be stored in memory  1030  in the computer readable storage medium  1050 . The program  1060  can include program modules  1064 . The program modules  1064  can generally carry out functions and/or methodologies of embodiments of the invention as described herein. The one or more programs  1060  are stored in memory  1030  and are executable by the processing unit  1020 . By way of example, the memory  1030  and are executable by the processing unit  1020 . By way of example, the memory  1030  may store an operating system  1052 , one or more application programs  1054 , other program modules, and program data on the computer readable storage medium  1050 . It is understood that the program  1060 , and the operating system  1052  and the application program(s)  1054  stored on the computer readable storage medium  1050  are similarly executable by the processing unit  1020 . 
     The computer  1010  may also communicate with one or more external devices  1074  such as a keyboard, a pointing device, a display  1080 , etc.; one or more devices that enable a user to interact with computer  1010 ; and/or any devices (e.g., network card, modem, etc.) that enables the computer  1010  to communicate with one or more computing devices. Such communication can occur via the Input/Output (I/O) interfaces  1022 . Still yet, the computer  1010  can communicate with one or more networks  1200  such as local area network (LAN), a general wide area network (WAN), and/or a public network (e.g., the Internet) via network adapter/interface  1026 . As depicted, network adapter  1026  communicates with the other components of the computer  1010  via bus  1014 . It should be understood that although not shown, other hardware and/or software components could be used in conjunction with the computer  1010 . Examples, include, but are not limited to: microcode, device drivers  1024 , redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc. 
     It is understood that a computer or a program running on the computer  1010  may communicate with a server, embodied as the server  1100 , via one or more communications networks, embodied as the communications network  1200 . The communications network  1200  may include transmission media and network links which include, for example, wireless, wired or optical fiber, and routers, firewalls, switches, and gateway computers. The communications network may include connections, such as wire, wireless communication links, or fiber optic cables. A communications network may represent a worldwide collection of networks and gateways, such as the Internet, that use various protocols to communicate with one another, such as Lightweight Directory Access Protocol (LDAP), Transport Control Protocol/Internet Protocol (TCP/IP), Hypertext Transport Protocol (HTTP), Wireless Application Protocol (WAP), etc. A network may also include a number of different types of networks, such as, for example, an intranet, a local area network (LAN), or a wide area network (WAN). 
     In one example, a computer can use a network which may access a website on the Web (World Wide Web) using the Internet. In one embodiment, a computer  1010 , including a mobile device, can use a communications system or network  1200  which can include the Internet, or a public switched telephone network (PSTN) for example, a cellular network. The PSTN may include telephone lines, fiber optic cables, microwave transmission links, cellular networks, and communications satellites. The Internet may facilitate numerous searching and texting techniques, for example, using a cell phone or laptop computer to send queries to search engines via text messages (SMS), Multimedia Messaging Service (MMS) (related to SMS), email, or a web browser. The search engine can retrieve search results, that is, links to websites, documents, or other downloadable data that correspond to the query, and similarly, provide the search results to the user via the device as, for example, a web page of search results. 
     The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. 
     It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later depicted. 
     Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models. 
     Characteristics are as follows: 
     On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service&#39;s provider. 
     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). 
     Resource pooling: the provider&#39;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). 
     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. 
     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. 
     Service Models are as follows: 
     Software as a Service (SaaS): the capability provided to the consumer is to use the provider&#39;s applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings. 
     Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations. 
     Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls). 
     Deployment Models are as follows: 
     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. 
     Community cloud: the cloud infrastructure is shared by several organizations and supports a 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 and may exist on-premises or off-premises. 
     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. 
     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). 
     A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes. 
     Referring now to  FIG. 8 , an illustrative cloud computing environment  50  is depicted. As shown, cloud computing environment  50  includes one or more cloud computing nodes  10  with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA) or cellular telephone  54 A, desktop computer  54 B, laptop computer  54 C, and/or automobile computer system  54 N may communicate. Nodes  10  may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid cloud as described hereinabove, or a combination thereof. This allows cloud computing environment  50  to offer infrastructure, platforms and/or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices  54 A-N shown in  FIG. 8  are intended to be illustrative only and that computing nodes  10  and cloud computing environment  50  can communicate with any type of computerized device over any type of network and/or network addressable connection (e.g., using a web browser). 
     Referring now to  FIG. 9 , a set of functional abstraction layers provided by cloud computing environment  50  ( FIG. 8 ) is shown. It should be understood in advance that the components, layers, and functions shown in  FIG. 9  are intended to be illustrative only and embodiments of the invention are not limited thereto. As depicted, the following layers and corresponding functions are provided: 
     Hardware and software layer  60  includes hardware and software components. Examples of hardware components include: mainframes  61 , RISC (Reduced Instruction Set Computer) architecture based servers  62 ; servers  63 ; blade servers  64 ; storage devices  65 ; and networks and networking components  66 . In some embodiments, software components include network application server software  67  and database software  68 . 
     Virtualization layer  70  provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers  71 ; virtual storage  72 ; virtual networks  73 ; including virtual private networks; virtual applications and operating systems  74 ; and virtual clients  75 . 
     In one example management layer  80  may provide the functions described below. Resource provisioning  81  provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing  82  provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal  83  provides access to the cloud computing environment for consumers and system administrators. Service level management  84  provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment  85  provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA. 
     Workloads layer  90  provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation  91 ; software development and lifecycle management  92 ; virtual classroom education delivery  93 ; data analytics processing  94 ; transaction processing  95 ; and  96  digital video content relationship mapping. 
     The present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention. 
     The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire. 
     Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device. 
     Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user&#39;s computer, partly on the user&#39;s computer, as a stand-alone software package, partly on the user&#39;s computer and party on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user&#39;s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention. 
     Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions. 
     These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the function/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devise to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks. 
     The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the function/acts specified in the flowchart and/or block diagram block or blocks. 
     The flowchart and block diagrams in the Figures illustrate the architecture, functionality and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the function noted in the blocks may occur out of the order noted in the Figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions.