Patent Application
20230156296
The present disclosure is related to the field of digital rights management.
Television programming and multimedia subscription services are often provided to customers in an all-or-nothing format: either a customer must subscribe to the entire package of digital content (e.g., channels) or subscribe to nothing at all. One of the reasons for this current structure in broadcast programming and Internet streaming services is the relative ease of implementing a digital rights management (“DRM”) scheme to control this binary subscription structure. A DRM system merely needs to check whether a certain customer has access to the selected programming or not. As a result of this subscription structure, many customers are forced into an all-or-nothing decision for subscribing to broadcast programming and/or Internet streaming. Customers who may not necessarily consume as much multimedia content as other customers may consume a disproportionate amount of multimedia resources relative to the amount of multimedia they consume. In other words, technical resources associated with broadcast programming and Internet streaming management must always provide access to certain programming, even though the customer is not consuming the multimedia at certain times, and/or the multimedia that is available to consume at certain times is not relevant to the customer's preferences. Modern multimedia DRM schemes fail to account for these alternative customer behaviors and alternative subscription schemes. As such, there is an increased need for systems and methods that can address the challenges of present-day multimedia subscription models and paradigms.
It is with respect to these and other general considerations that the aspects disclosed herein have been made. Also, although relatively specific problems may be discussed, it should be understood that the examples should not be limited to solving the specific problems identified in the background or elsewhere in the disclosure.
Non-limiting and non-exhaustive examples are described with reference to the following figures.
Various aspects of the disclosure are described more fully below with reference to the accompanying drawings, which form a part hereof, and which show specific exemplary aspects. However, different aspects of the disclosure may be implemented in many different forms and should not be construed as limited to the aspects set forth herein; rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the aspects to those skilled in the art. Aspects may be practiced as methods, systems, or devices. Accordingly, aspects may take the form of a hardware implementation, an entirely software implementation or an implementation combining software and hardware aspects. The following detailed description is, therefore, not to be taken in a limiting sense.
Embodiments of the present application are directed at systems and methods associated with temporally restricting and providing access to multimedia. In some embodiments, the temporal restriction may be based on certain days of the week. For example, a certain DRM scheme may restrict access to multimedia during the days of Monday to Friday but allow access to those same multimedia items on Saturday and Sunday. Throughout the present disclosure, the term “multimedia item” may refer to certain channels, TV shows, movies, songs, pictures, and other multimedia items.
In other embodiments, a DRM system may be implemented to consider a user's profile and intelligently restrict and provide access to certain multimedia items based on a user's profile. A user's profile may be constructed using at least one machine-learning algorithm that may be trained on a user's previous multimedia viewing history. For example, a user may be subscribed to a certain temporal subscription for multimedia content (e.g., a subscription that permits a user to view certain multimedia only on the weekends). During the week, if the user desires to access certain multimedia items that are restricted, then the DRM system may intelligently provide an option for the user to access the restricted multimedia. This option from the DRM system may be provided in the form of a notification on a screen (i.e., a pop-up), and in some embodiments, the pop-up may request the user upgrade the user's subscription or pay a fee to access the restricted multimedia items.
In further embodiments, the DRM system may provide an option for a user to access specific multimedia items, like a certain live TV event (e.g., NFL football game, livestreamed concert, State of the Union address, etc.), that otherwise would be restricted to the user based on the user's subscription tier. This one-time access to a certain multimedia item may require a one-time fee from the user; in other embodiments, the multimedia item may be provided to the user as a complimentary multimedia item for that specified time.
To manage these different subscription tiers, certain data structures may be used. For instance, a hash table may be constructed with a link to a user profile, as well as a subscription tier indicator, that may be retrieved by the DRM system to then allow or restrict certain multimedia content based on the subscription tier. Accordingly, the present disclosure provides a plurality of technical benefits including but not limited to: construction of efficient data structures to manage different subscription models; reduce broadcast programming and network resources/bandwidth by implementing smart DRM systems; and reducing programming bloat by providing access to certain digital content on a user-specific basis based on machine-learning algorithms.
Client devices 102, 104, and 106 may be configured to run a DRM program that provides and restricts access to certain multimedia items. In aspects, a client device, such as client devices 102, 104, and 106, may have access to one or more data sources and/or databases comprising multimedia content. In other aspects, client devices 102, 104, and 106, may be equipped to receive live broadband and/or satellite signals carrying multimedia items to be displayed on a display device, such as a television or client devices 102, 104, and 106. The signals that client devices 102, 104, and 106 may receive may be transmitted from satellite 122. Satellite 122 may also be configured to communicate with network(s) 108, in addition to being able to communicate directly with client devices 102, 104, and 106. In some examples, a client device may be a set-top box that is connected to a display device, such as a television (or a television that may have set-top box circuitry built into the television mainframe).
Client devices 102, 104, and 106 may be configured to run DRM software that may be stored locally on the client device or run remotely via network(s) 108. For example, a client device may receive a signal from satellite 122 containing a particular multimedia item to be displayed. The client device may attempt to play and display the multimedia item, but the DRM software may restrict access to this multimedia item based on, among other factors, the client device's current location, the current day/time, and a user's subscription level or tier. In alternative scenarios, the DRM application may be deployed via the cloud over network(s) 108 when client device(s) 102, 104, and 106 are connected to the Internet. The system may receive a certain request to display a multimedia item, but based on information received from a user's profile and subscription preferences (e.g., which may be accessed by the DRM application via remote server(s) 116, 118, and/or 120), the multimedia item may be restricted from display on the client device(s). If the client device(s) are offline, a local DRM application may be stored natively on the client device(s) 102, 104, and/or 106 in local databases 110, 112, and/or 114.
In some example aspects, the DRM application running on client device(s) 102, 104, and/or 106 may be configured to receive an electronic programming guide (EPG) via network(s) 108 and/or satellite 122. The EPG may display certain multimedia items that may be restricted to a user based on a user's subscription level, a timezone of a user device, a billing zip code of a user's profile, and/or a geographic location of a user device.
In yet further example aspects, the DRM system described herein may automatically and intelligently provide access to certain multimedia items based on at least one machine-learning algorithm trained on at least one dataset reflecting a user's multimedia viewing preferences. The at least one machine-learning algorithms (and models) may be stored locally at databases 110, 112, and/or 114 and/or externally at databases 116, 118, and/or 120. Client devices 102, 104, and/or 106 may be equipped to access these machine learning algorithms and intelligently select particular multimedia items to be displayed (e.g., for purchase and/or viewing, generally) based on at least one machine-learning model that is trained on a user's historical viewing history. For example, if a user frequently watches NFL football games, the user's viewing history may be collected to train a machine-learning model to then automatically provide certain digital items to the user for access during the temporal timeframes that the user would otherwise be restricted from accessing those certain digital items. Specifically, the DRM system may provide a user the option to view a Monday night football game that the user otherwise cannot view because the user's subscription level limits the user to viewing certain multimedia items to Saturday and Sunday. The DRM system may provide a pop-up to the user that describes how the user may access the currently restricted multimedia content. The user may be prompted to upgrade his/her subscription level and/or pay a one-time fee to view the multimedia item. In other example aspects, a user's viewing history may demonstrate a preference for certain genres, actors, time periods, geographies, etc. The DRM systems and methods described herein may implement at least one machine-learning algorithm that was trained on a dataset reflecting a user's preferences regarding genres, actors, time periods, and geographies, among other factors. The systems and methods described herein may then automatically select particular restricted multimedia items to display to the user if the user upgrades his/her subscription level, or, e.g., pays a one-time fee. Other multimedia items may include live events, such as livestreamed music concerts, political events (e.g., State of the Union address), holiday events (e.g., Thanksgiving parades, New Years' Eve, etc.), exclusive interviews with celebrities, as well as new episodes and seasons of television shows that a user may be inclined to watch based on the user's past viewing history and profile data.
As described herein, a machine-learning (ML) model may refer to a predictive or statistical utility or program that may be used to determine a probability distribution over one or more character sequences, classes, objects, result sets or events, and/or to predict a response value from one or more predictors. A model may be based on, or incorporate, one or more rule sets, machine learning, a neural network, or the like. In examples, the ML models may be located on the client device, service device, a network appliance (e.g., a firewall, a router, etc.), or some combination thereof. The ML models may process user viewing history and other data stores of user preferences (e.g., social media profiles) to determine which multimedia content should be presented to the user, or, in some examples, suggested to the user for purchase and viewing outside of the subscription timeframe (e.g., viewing multimedia items on weekdays when the subscription tier limits a user's viewing of multimedia items to the weekend). Determining whether a certain multimedia item should be presented by the DRM system may comprise identifying various characteristics of a user's viewing history and preferences. For instance, if a user has a social media profile that displays several photographs of the user engaging in sports, then the DRM system described herein may determine that certain program from sports channels (e.g., ESPN, NFL network) should be presented. Similarly, if a user posts certain social media items reflecting a favorite sports team, the DRM system may determine that games in which that specific team is playing should be presented (e.g., a certain football team is playing on a Monday night or a Thursday night, so the DRM system will prompt the user to view that multimedia item by upgrading his/her subscription level). Based on an aggregation of data from a user's viewing history, social media profiles, and other user data stores, at least one ML model may be trained and subsequently deployed to automatically suggest for presentation multimedia items that a particular user may enjoy viewing at a later time.
The trained ML model may be deployed to one or more devices. As a specific example, an instance of a trained ML model may be deployed to a server device and to a client device. The ML model deployed to a server device may be configured to be used by the client device when, for example, the client device is connected to the Internet. Conversely, the ML model deployed to a client device may be configured to be used by the client device when, for example, the client device is not connected to the Internet. In some instances, a client device may not be connected to the Internet but still configured to receive satellite signals with streaming information, multimedia information, and channel guides. In such examples, the ML model may be locally cached by the client device.
Because satellite 122 and network(s) 108 are configured to communicate with one another, the systems and methods described herein may be able to assess when a particular multimedia item is scheduled to air on live television (via satellite 122) or stream via an Internet-streaming service through network(s) 108. In such situations, the DRM system may initiate a notification beforehand (e.g., during the temporal timeframe the user has access to the multimedia content) to prompt the user to upgrade a subscription so the user may be able to view the upcoming multimedia item outside of the permitted timeframe of the user's subscription. The notification may display on a client device 102, 104, and/or 106 informing the user that the multimedia item is upcoming and is available for viewing by upgrading a certain subscription and/or remitting a one-time fee.
Memory 205 can store instructions for running one or more applications or modules on processor(S) 210. For example, memory 205 could be used in one or more embodiments to house all or some of the instructions needed to execute the functionality of DRM module 215 and communications module 220. Generally, memory 205 can include any device, mechanism, or populated data structure used for storing information. In accordance with some embodiments of the present disclosures, memory 205 can encompass, but is not limited to, any type of volatile memory, nonvolatile memory, and dynamic memory. For example, memory 205 can be random access memory, memory storage devices, optical memory devices, magnetic media, floppy disks, magnetic tapes, hard drives, SIMMs, SDRAM, RDRAM, DDR, RAM, SODIMMs, EPROMs, EEPROMs, compact discs, DVDs, and/or the like. In accordance with some embodiments, memory 205 may include one or more disk drives, flash drives, one or more databases, one or more tables, one or more files, local cache memories, processor cache memories, relational databases, flat databases, and/or the like. In addition, those of ordinary skill in the art will appreciate many additional devices and techniques for storing information that can be used as memory 205.
DRM module 215 may be configured to permit and restrict access to certain multimedia content based on a variety of factors. Some factors include, but are not limited to, days of the week, a geographic location of a user device, billing zip code of a user profile, a user's profile preferences, certain subject matter of multimedia content, and remittance of a fee, among other factors. DRM module 215 may be configured to store certain subscription values and user profiles in a data structure, such as a hash table. The hash table may be configured to organize certain users based on subscription tier. In one embodiment, the subscription tier of certain users may be denoted by a binary indicator (e.g., 0 or 1) to indicate if they have unlimited access to a certain set of multimedia content or no access to that set of multimedia content. Another column in the hash table may be a further binary indicator regarding a particular subscription type, such as a “weekend access” subscription type. In this column in the table, a further binary indicator may indicate if a user is a “weekend access” subscriber or not. When providing multimedia content to a user's device, DRM module 215 may refer to a hash table to determine which multimedia content may be accessible by a user and when that multimedia content may be accessible by the user.
In further embodiments, DRM module 215 may be configured with at least one ML engine that can receive user viewing history and other user profile data to train at least one machine-learning model or compare user viewing history and profile data against an already-trained machine-learning model. For example, to train the ML model(s), the extracted features of the input data may be associated with specific multimedia identifiers (e.g., director, writer, genre, geography, actor/actresses, etc.). The ML engine may utilize various machine learning algorithms to train the ML model, including but not limited to linear regression, logistic regression, linear discriminant analysis, classification and regression trees, naïve Bayes, k-Nearest neighbors, learning vector quantization, neural networks, support vector machines (SVM), bagging and random forest, and/or boosting and AdaBoost, among other supervised and unsupervised machine learning algorithms. The aforementioned machine learning algorithms may also be applied when comparing input data to an already-trained machine learning model. Based on the identified and extracted multimedia features and patterns (e.g., user viewing patterns), ML engine may select the appropriate machine learning algorithm to apply to the multimedia features to train the at least one machine learning model. For example, fi the received multimedia features are complex and demonstrate non-linear relationships, then ML engine may select a bagging and random forest algorithm to train the machine learning model. However, if the multimedia features demonstrate a linear relationship to certain multimedia features and input data (e.g., user viewing history), then ML engine may apply a linear or logistic regression algorithm to train the machine learning model.
In other aspects, the ML engine incorporated within the DRM module 215 may apply at least one already-trained machine learning model to the received multimedia features from the past user viewing history and other user profile data to detect previously identified and extracted multimedia features (e.g., certain actors the user may enjoy watching in movies or television shows) and previously recognized patterns (e.g., user enjoys NFL football). ML engine may be configured to compare at least one trained machine learning model to the received input data to generate comparison results that indicate which multimedia items should be presented to the user during the temporal timeframe the user does not have access to certain multimedia content based on the user's subscription tier and based on the user's preferences. Specifically, ML engine may compare the identified and extracted multimedia features of the multimedia items from the user viewing history that are associated with specific multimedia identifiers. ML engine is also configured to generate comparison results, indicating the similarity (or lack thereof) between certain multimedia assets on which the machine learning model was trained and the received multimedia features. In other aspects, the comparison results may include a confidence indicator, which may indicate how confident the system may be that a candidate multimedia item is relevant to the user. For instance, based on the user viewing history and other user profile data, a confidence score may be assigned to certain candidate multimedia items to be displayed (e.g., via a pop-up notification). In one example, candidate multimedia items that are scored with a confidence indicator above a certain threshold may be presented to the user, whereas those below the threshold may not be presented (e.g., multimedia items with an “8/10” or higher on the confidence score may be displayed to the user as an option to view during restricted viewing times). It should be appreciated that DRM module 215 (which houses the ML engine) is configured to communicate with communications module 220.
Communications module 220 is associated with sending/receiving information (e.g., collected by DRM module 215, such as commands received via client devices or remotes, navigation history within the EPG, etc.) with other client devices, remote web servers, set-top boxes, televisions, remotes, etc. These communications can employ any suitable type of technology, such as Bluetooth, WiFi, WiMax, cellular (e.g., 5G), single hop communication, multi-hop communication, Dedicated Short Range Communications (DSRC), or a proprietary communication protocol. In some embodiments, communications module 220 sends information received by DRM module 215 to client device(s) 102, 104, and/or 106, as well as remote server(s) 116, 118, and/or 120 via network(s) 108 and/or satellite 122. In other examples, communications module 220 may communicate a command to DRM module 215 that is received from a client device, remote web server, remote, television, and/or other mechanism initiating a command to display, navigate, and/or dismiss an EPG.
In yet other embodiments, the DRM system described herein may provide notifications to a user during a permitted timeframe (e.g., while a user is watching accessible multimedia content) that prompt the user to preemptively gain access to certain multimedia content that will be restricted in the future under the user's current subscription tier. For example, a user who is watching a football game on Sunday and has a weekend-only subscription package, may receive from the DRM system an intelligent notification to upgrade his or her subscription tier so that the user can view an upcoming football game on Monday night (which would be restricted under the user's current subscription tier). The notification may be generated by the DRM system based on at least one ML model that is trained on the user's past viewing history. For example, if the user frequently watches football on the weekend, then a notification to prompt the user to watch a football game during the week may be generated by the DRM system based on the ML model. If a user frequently watches music event son the weekend, then a livestreamed concert event occurring during the week may be of interest to the user according to the ML model. The ML model may provide the suggestion to the DRM system, which may then generate a notification to the user to upgrade the user's subscription in order to access that multimedia item during the week.
In its most basic configuration, operating environment 600 typically includes at least one processing unit 602 and memory 604. Depending on the exact configuration and type of computing device, memory 604 (storing, among other things, information related to devices, blockchain networks, service performance indicators, remedial actions, and instructions to perform the methods disclosed herein) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in
Operating environment 600 typically includes at least some form of computer readable media. Computer readable media can be any available media that can be accessed by processing unit 602 or other devices comprising the operating environment. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures (e.g., blockchains), program modules or other data. Computer storage media includes, RAM, ROM EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage, or other magnetic storage devices, or any other tangible medium which can be used to store the desired information. Computer storage media does not include communication media.
Communication media embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulate data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media.
The operating environment 600 may be a single computer (e.g., mobile computer) operating in a networked environment using logical connections to one or more remote computers. The remote computer may be a personal computer, a server, a router, a network PC, a peer device, an OTA antenna, a set-top box, or other common network node, and typically includes many or all of the elements described above as well as others not so mentioned. The logical connections may include any method supported by available communications media. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet.
Aspects of the present disclosure, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to aspects of the disclosure. The functions/acts noted in the blocks may occur out of the order as shown in any flowchart. 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/acts involved.
The description and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the disclosure as claimed in any way. The aspects, examples, and details provided in this application are considered sufficient to convey possession and enable others to make and use the best mode of the claimed disclosure. The claimed disclosure should not be construed as being limited to any aspect, example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present application, one skilled in the art may envision variations, modifications, and the alternate aspects falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope of the claimed disclosure.
From the foregoing, it will be appreciated that specific embodiments of the invention have been described herein for purposes of illustration, but that various modifications may be made without deviating from the scope of the invention. Accordingly, the invention is not limited except as by the appended claims.
This application claims priority to and the benefit of U.S. Provisional Patent Application No. 63/281,025, filed on Nov. 18, 2021, entitled “SYSTEMS AND METHODS FOR TEMPORALLY RESTRICTING ACCESS TO MULTIMEDIA,” which is incorporated herein by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63281025 | Nov 2021 | US |
