Patent Publication Number: US-2023156276-A1

Title: Systems and methods to determine reduction of interest in a content series

Description:
BACKGROUND 
     The present disclosure is directed to computer-implemented techniques for determining reduction of interest in a content series and to increasing the interest upon such determination. 
     SUMMARY 
     Television programs typically have large viewership in the first few seasons, but as time progresses, some viewers lose interest, causing viewership to decline. If viewership of a television program declines, the television program could end up losing valuable production investments and may ultimately be canceled. With the large amounts of time and money used in the production of television programs, there exists a need for improved systems and methods to determine a viewer&#39;s reduction of interest in a content series. 
     Accordingly, techniques are disclosed herein for systems and methods to determine a reduction of interest in a content series. The various embodiments disclosed herein provide a computer-implemented method for determining interest in a content series. To begin, the processing circuitry monitors a user&#39;s consumption of a content series and associates the consumption with a profile. Using the data gained from monitoring the consumption of the content series, the processing circuitry determines a pattern of consumption associated with the user&#39;s consumption. If there is a change in the pattern of consumption that indicates a reduction of interest, the processing circuitry identifies the change in the pattern. The processing circuitry then determines contextual information related to the content series, based at least in part on the change in pattern, and identifies a reason for the reduction of interest. For example, a system can monitor a user who is a regular viewer of the television program Criminal Minds. The system notices that the user watches Criminal Minds every Friday. After season two, the system identifies that the user stopped watching criminal minds, which is an example of a change in the viewing habits of the user for that content series. The system uses the change in pattern and checks it against contextual information relating to Criminal Minds. Contextual information for Criminal Minds may include actors leaving the program, change in the program&#39;s time slot, premier of other related programs, or similar such information. In this example, the system determines that the user stopped watching Criminal Minds at the same time Mandy Patinkin left the program. Accordingly, the system identifies that Mandy Patinkin leaving Criminal Minds was the reason the user had reduced interest in the program. 
     The various embodiments disclosed herein also provide a computer-implemented method for increasing interest in a content series. To begin, the processing circuitry monitors a user&#39;s consumption of a content series and associates the consumption with a profile. If there is a reduction in interest of the user, the process circuitry uses the data gained from monitoring the consumption of the content series to detect the reduction of interest. Using a trained machine learning model, the processing circuitry identifies a reason for the reduction of interest. Then the processing circuitry causes an operation to be performed, such as providing a spoiler, where the parameters of the operation are selected based on the reason for the reduction of interest. For example, a system can monitoring a user who is a regular viewer of Criminal Minds. The system notices that the user has stopped watching Criminal Minds. Using a trained machine learning model, the system identifies that the reason the user stopped watching Criminal Minds was because Mandy Patinkin left the program. The system can then cause a commercial to be shown to the user indicating that Mandy Patinkin is returning to the program in the new season of Criminal Minds, thus reengaging the user. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The below and other objects and advantages of the disclosure will be apparent upon consideration of the following detailed description, taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout, and in which: 
         FIG.  1    shows a block diagram of an illustrative process for determining a reduction of interest in a content series, in accordance with some embodiments of the disclosure; 
         FIG.  2    shows a block diagram of an illustrative process for determining a reduction of interest in a content series and providing a spoiler, in accordance with some embodiments of the disclosure; 
         FIGS.  3 A and  3 B  show illustrative diagrams of displaying spoilers on devices, in accordance with some embodiments of the disclosure; 
         FIG.  4    shows an illustrative block diagram of a user equipment (UE) device system, in accordance with some embodiments of the disclosure; 
         FIG.  5    shows an illustrative block diagram of a media system, in accordance with some embodiments of the disclosure; 
         FIG.  6    is an illustrative flowchart of a process for determining a reduction of interest in a content series, in accordance with some embodiments of the disclosure. 
         FIG.  7    is an illustrative flowchart of a process for determining a reduction of interest in a content series and causing an operation to be performed, in accordance with some embodiments of the disclosure; and 
         FIG.  8    is an illustrative flowchart of a process for determining a reduction of interest in a content series and providing a spoiler based on the reduction of interest, in accordance with some embodiments of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
       FIG.  1    shows a block diagram  100  of an illustrative process for determining a reduction of interest in a content series, in accordance with some embodiments of the disclosure. In an embodiment, the interest module  102  receives consumption data  104  of a content series, which is collected by monitoring consumption of a content series. In some embodiments, the consumption data  104  may be collected at or by the interest module  102 . In some embodiments, the consumption is associated with a profile wherein the profile is associated with a user. A content series may include television programs, movies, audio books, podcasts, playlists, songs, videos, similar such content, and any combination thereof. Consumption of a content series may include frequency of consumption, time period of consumption, similar such consumption matrixes, or any combination thereof. 
     In  FIG.  1   , the content series is Criminal Minds, and the consumption data  104  is represented by the number of hours per week the user watches Criminal Minds. The interest module  102  uses the consumption data  104  to determine a pattern of the consumption. In this embodiment, the interest module  102  determines that a pattern of consumption is a user consuming three hours of content a week in the form of watching Criminal Minds episodes. In an embodiment, this determination is made in real time as the interest module  102  receives the consumption data  104 , and the determination of the pattern can change as new consumption data is received. The interest module  102  uses the consumption data  104  to identify if a change in the pattern of consumption indicative of a reduction of interest takes place. In this embodiment, the interest module  102  identifies that after week seven, the number of hours per week that the user watches Criminal Minds declines. The interest module  102  identifies that this change in consumption data  104  indicates a reduction in interest of the user. 
     Based in part on the change in the pattern of the consumption data  104 , the interest module  102  determines contextual information related to the content series. In this embodiment, similar to the consumption data  104 , the interest module  102  receives contextual information  106  related to the content series. The contextual information  106  may include actors leaving the program, change in the program&#39;s time slot, premier of other related programs, similar such information, or a combination thereof. The interest module  102  receives a plurality of contextual information  106  and determines which of the plurality of contextual information relates to the change in the pattern identified from the consumption data  104 . The interest module  102  determines that the change in the pattern at week seven coincides with contextual information about Criminal Minds that actor Mandy Patinkin left the program at week seven. Accordingly, the interest module identifies a reason  108  for the reduction of interest. In this embodiment, the interest module  102  identifies Mandy Patinkin leaving Criminal Minds as the reason  108  for the change in the pattern of consumption. 
       FIG.  2    shows a block diagram  200  of an illustrative process for determining reduction of interest in a content series and providing a spoiler, in accordance with some embodiments of the disclosure. In this embodiment, the interest module  202  receives consumption data  204  and contextual information  206  in the same manner described in  FIG.  1   . In this embodiment, the interest module  202  identifies a reason for the reduction of interest using a trained machine learning module. Similar to  FIG.  2   , the interest model  202 , identifies Mandy Patinkin leaving Criminal Minds as the reason for the reduction of interest. The interest module  202  then performs an operation to reengage the user, wherein the parameters of the operation are related to the reason for the reduction of interest. In this embodiment, the interest module  202  causes a spoiler to be displayed on a user device. Here the spoiler is a push notification  208 , but spoilers can include commercials, promotional videos, phone calls, audio advertisements, multimedia messages, similar such content, or a combination thereof. The spoiler can be displayed on a smartphone  210 , or any similar device. Because the reason for the reduction of interest was Mandy Patinkin leaving the show, the interest module  202  determined that the spoiler “Mandy Patinkin Returns” would relate to this reason and reengage the user. 
       FIGS.  3 A and  3 B  show illustrative diagrams of displaying spoilers on devices, in accordance with some embodiments of the disclosure.  FIG.  3 A  shows a smartphone device  304  displaying a notification  302  with a spoiler.  FIG.  3 B  shows a television  308  displaying a commercial  306  with a spoiler. 
       FIGS.  4 - 5    describe exemplary devices, systems, servers, and related hardware for determining a reduction of interest in a content series and to increasing the interest upon such determination, in accordance with some embodiments.  FIG.  4    shows a generalized embodiment of a user equipment device  400 , in accordance with some embodiments. 
     The user equipment device  400  may receive content and data via input/output (hereinafter “I/O”) path  402 . The I/O path  402  may provide audio content (e.g., broadcast programming, on-demand programming, Internet content, content available over a local area network (LAN) or wide area network (WAN), and/or other content) and data to control circuitry  404 , which includes processing circuitry  406  and storage  408 . The control circuitry  404  may be used to send and receive commands, requests, and other suitable data using the I/O path  402 . The I/O path  402  may connect the control circuitry  404  (and specifically the processing circuitry  406 ) to one or more communications paths (described below). I/O functions may be provided by one or more of these communications paths but are shown as a single path in  FIG.  4    to avoid overcomplicating the drawing. 
     The control circuitry  404  may be based on any suitable processing circuitry such as the processing circuitry  406 . As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc., and may include a multi-core processor (e.g., dual-core, quad-core, hexa-core, or any suitable number of cores) or supercomputer. In some embodiments, processing circuitry may be distributed across multiple separate processors or processing units, for example, multiple of the same type of processing units (e.g., two Intel Core i7 processors) or multiple different processors (e.g., an Intel Core i5 processor and an Intel Core i7 processor). The interest module can be at least partially implemented using the control circuitry  404 . The interest module is any suitable software, hardware, or both that implements the features of the present disclosure related to monitoring interest in content as well as associated functionality described herein, including finding reasons for reduced interest, and performing operations in response to a reduction of interest. The interest module can be implemented on user equipment, on remote servers, or across both. 
     In client/server-based embodiments, the control circuitry  404  may include communications circuitry suitable for communicating with one or more servers that may at least implement an interest module. The instructions for carrying out the above-mentioned functionality may be stored on the one or more servers. Communications circuitry may include a cable modem, an integrated service digital network (ISDN) modem, a digital subscriber line (DSL) modem, a telephone modem, Ethernet card, or a wireless modem for communications with other equipment, or any other suitable communications circuitry. Such communications may involve the Internet or any other suitable communications networks or paths. In addition, communications circuitry may include circuitry that enables peer-to-peer communication of user equipment devices, or communication of user equipment devices in locations remote from each other (described in more detail below). 
     Memory may be an electronic storage device provided as the storage  408  that is part of the control circuitry  404 . As referred to herein, the phrase “electronic storage device” or “storage device” should be understood to mean any device for storing electronic data, computer software, or firmware, such as random-access memory, read-only memory, hard drives, optical drives, digital video disc (DVD) recorders, compact disc (CD) recorders, BLU-RAY disc (BD) recorders, BLU-RAY 3D disc recorders, digital video recorders (DVRs, sometimes called personal video recorders, or PVRs), solid-state devices, quantum storage devices, gaming consoles, gaming media, or any other suitable fixed or removable storage devices, and/or any combination of the same. The storage  408  may be used to store various types of content described herein. Nonvolatile memory may also be used (e.g., to launch a boot-up routine and other instructions). Cloud-based storage, described in relation to  FIG.  5   , may be used to supplement the storage  408  or instead of the storage  408 . 
     The control circuitry  404  may include audio generating circuitry and tuning circuitry, such as one or more analog tuners, audio generation circuitry, filters or any other suitable tuning or audio circuits or combinations of such circuits. The control circuitry  404  may also include scaler circuitry for upconverting and down converting content into the preferred output format of the user equipment device  400 . The control circuitry  404  may also include digital-to-analog converter circuitry and analog-to-digital converter circuitry for converting between digital and analog signals. The tuning and encoding circuitry may be used by the user equipment device  400  to receive and to display, play, or record content. The circuitry described herein, including, for example, the tuning, audio generating, encoding, decoding, encrypting, decrypting, scaler, and analog/digital circuitry, may be implemented using software running on one or more general purpose or specialized processors. If the storage  408  is provided as a separate device from the user equipment device  400 , the tuning and encoding circuitry (including multiple tuners) may be associated with the storage  408 . 
     The user equipment device  400  may optionally include a user interface  410 . The interface  410  may be any suitable user interface, such as a remote control, mouse, trackball, keypad, keyboard, touchscreen, touchpad, stylus input, joystick, or other user input interfaces. A display  412  may be provided as a stand-alone device or integrated with other elements of the user equipment device  400 . For example, the display  412  may be a touchscreen or touch-sensitive display. In such circumstances, the interface  410  may be integrated with or combined with the microphone  416 . When the interface  410  is configured with a screen, such a screen may be one or more of a monitor, a television, a liquid crystal display (LCD) for a mobile device, active matrix display, cathode ray tube display, light-emitting diode display, organic light-emitting diode display, quantum dot display, or any other suitable equipment for displaying visual images. In some embodiments, the interface  410  may be HDTV-capable. In some embodiments, the display  412  may be a 3D display. The speaker (or speakers)  414  may be provided as integrated with other elements of user equipment device  400  or may be a stand-alone unit. 
     The user equipment device  400  of  FIG.  4    can be implemented in system  500  of  FIG.  5    as user device  502 , but any other type of user equipment suitable for determining reduction of interest in a content series may be used. For example, user equipment devices such as television equipment, wireless user communication devices, or similar such devices may be used. User equipment devices may be part of a network of devices. Various network configurations of devices may be implemented and are discussed in more detail below. 
     In the system  500 , there can be more than one user equipment device but only one is shown in  FIG.  5    to avoid overcomplicating the drawing. In addition, a user may utilize more than one type of user equipment device and more than one of each type of user equipment device. In an embodiment, there may be paths between user equipment devices, so that the devices may communicate directly with each other via communications paths, as well as other short-range point-to-point communications paths, such as USB cables, IEEE 1394 cables, wireless paths (e.g., Bluetooth, infrared, IEEE 802-11x, etc.), or other short-range communication via wired or wireless paths. BLUETOOTH is a certification mark owned by Bluetooth SIG, INC. In an embodiment, the user equipment devices may also communicate with each other directly through an indirect path via the communications network  506 . 
     The user equipment devices may be coupled to communications network  506 . Namely, the user device  502  is coupled to the communications network  506  via communications path  504 . The communications network  506  may be one or more networks including the Internet, a mobile phone network, mobile voice or data network (e.g., a 4G or LTE network), cable network, public switched telephone network, or other types of communications network or combinations of communications networks. The path  504  may separately or together with other paths include one or more communications paths, such as, a satellite path, a fiber-optic path, a cable path, a path that supports Internet communications (e.g., IPTV), free-space connections (e.g., for broadcast or other wireless signals), or any other suitable wired or wireless communications path or combination of such paths. In one embodiment path  504  can be a wireless path. Communications with the user equipment device may be provided by one or more communications paths but is shown as a single path in  FIG.  5    to avoid overcomplicating the drawing. 
     The system  500  also includes content source  512 , and server  514 , which can be coupled to any number of databases providing information to the interest module  102  in  FIG.  1   . The content source  512  represents any computer-accessible source of content, such as a storage for audio content, metadata, or similar such information. The server  514  may store and execute various software modules for implementing the interest module functionality of the interest module  102 . 
       FIG.  6    is an illustrative flowchart of a process  600  for determining a reduction of interest in a content series, in accordance with some embodiments of the disclosure. Process  600 , and any of the following processes, may be executed by control circuitry  404 . Control circuitry  404  may be part of an interest module  102 , or of a remote server separated from the interest module  102  by way of a communications network or distributed over a combination of both. In some embodiments, instructions for executing process  600  may be encoded onto a non-transitory storage medium (e.g., the storage  408 ) as a set of instructions to be decoded and executed by processing circuitry (e.g., the processing circuitry  406 ). Processing circuitry may, in turn, provide instructions to other sub-circuits contained within control circuitry  404 , such as the encoding, decoding, encrypting, decrypting, scaling, analog/digital conversion circuitry, and the like. It should be noted that the process  600 , or any step thereof, could be performed on, or provided by, any of the devices shown in  FIGS.  1 - 5   . Although the process  600  is illustrated as described as a sequence of steps, it is contemplated that various embodiments of process  600  may be performed in any order or combination and need not include all the illustrated steps. 
     At  602 , the interest module, by control circuitry  404 , monitors consumption of a content series, the consumption associated with a profile. In some embodiments, the profile represents one or more users. For example, a profile associated with a single user may have a piece of identifying information such as name, identification number, address, demographic, interests, similar such information, or a combination thereof. In some embodiments, a profile may represent a group of users. For example, a profile could represent all people in a certain region, all people who like horror movies, or all people with both characteristics. In some embodiments, a user equipment device  400  generates or assigns a profile to one or more users. In an embodiment, a user may create or select a profile for themselves. In some embodiments, the profile is associated with a social networking account of one or more users. In an embodiment, the profile is stored in storage  408  of a user equipment device  400 . In some embodiments, monitoring is performed by tracking different types of consumption of a content series including observing the content series, reading articles and/or blog posts related to the content series, posting on a social networking account about the content series, attending special events related to the content series, similar such activities, or a combination thereof. In some embodiments, the interest module receives consumption data from other user devices, media content sources  512 , and/or servers  514 , via the I/O path  402 . 
     At  604 , the interest module, by control circuitry  404 , determines a pattern of consumption of the content series. In some embodiments, the interest module uses the consumption data gained from step  602  to determine a pattern of consumption. In some embodiments, the interest module uses pattern recognition techniques to determine a pattern of consumption from the consumption data. In some embodiments, the interest module uses a trained machine learning model to determine a pattern of consumption from the consumption data. In some embodiments, this consumption data may be pre-processed and classified e.g., using any appropriate data mining classification algorithm (e.g., Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbors, Decision Tree, Random Forest, Neural Networks, Support Vector Machine, etc.). For example, a known data mining classification algorithm can be used to classify information for the interest module  102 . After the information is classified, it may be stored in, e.g., a classified information database. Periodically, when additional consumption data is received, the classified information database may be updated by classifying the additional consumption data. The types of identified patterns can vary to reflect different types of viewing habits. For example, if the type of consumption monitored is the amount of hours per week that a user watches a television program, the types of patterns can vary. Some users watch a television program as fast as possible (‘binge watchers”), other users watch a few episodes of the television program per week, while other users watch the television program&#39;s episodes as they air. In some embodiments, the determination of the pattern of consumption is performed on user devices  502 , media content sources  512 , and/or servers  514 , and is sent to the interest module. 
     At  606 , the interest module, by control circuitry  404 , identifies a change in the pattern indicative of a reduction of interest in the content series. In some embodiments, the interest module uses pattern recognition techniques as discussed above to identify a change in the pattern of consumption. In one embodiment, a trained machine learning model determines if there is a change in the pattern of consumption. In an embodiment, when the interest module determines a change in the pattern of consumption, the interest module must then determine if the change in the pattern is indicative of a reduction of interest. For example, a change in the pattern of consumption may be a decrease in the number of hours of consumption per week to below a threshold, wherein the threshold is determined by the interest module. In an embodiment, a change in a pattern of consumption may be that the frequency of consumption decreases over a time period. In some embodiments, the interest module recognizes only changes in the pattern of consumption indictive of a reduction of interest, for example if a user went from consuming two hours of content a week to five hours a week, the interest module would not recognize this as a change in the pattern of consumption and instead would just update the determined pattern of consumption accordingly. 
     At  608 , the interest module, by control circuitry  404 , determines contextual information related to the content series. In some embodiments, contextual information is stored in storage in the interest module. In some embodiments, contextual information is sent to the interest module though user devices  502 , media content sources  512 , and/or servers  514 . In some embodiments, contextual information may include actors leaving the program, changes in the program&#39;s time slot, premier of other related programs, new actors joining the program, changes in storyline, changes in pace of the program, changes in critic reviews, repeating of plotlines, or similar such information. In some embodiments, the interest module identifies a change in the pattern of consumption in step  606  and requests content information related, at least in part, to the change in pattern. The interest module may request this contextual information from user devices  502 , media content sources  512 , and/or servers  514 . In some embodiments, the interest module determines contextual information related to the change in consumption pattern from contextual information stored in storage on the interest module. 
     At  610 , the interest module, by control circuitry  404 , identifies a reason for reduction of interest. In some embodiments, the interest module uses the identified change in pattern and contextual information based at least in part on the change in pattern and identifies a reason for the reduction of interest. For example, if the change in the pattern begins at week seven, the interest module can use contextual information to determine that during week seven of the content series a main actor left the program. Accordingly, the interest module identifies the reason for the reduction of interest is the main actor leaving the program. 
       FIG.  7    is an illustrative flowchart of a process  700  for determining a reduction of interest in a content series and providing an operation to be performed, in accordance with some embodiments of the disclosure. At step  702 , the interest module, by control circuitry  404 , monitors consumption of a content series. The monitoring of consumption of the content series can use the methods and techniques described in step  602  above. 
     At  704 , the interest module, by control circuitry  404 , detects a reduction of interest in the content series. In some embodiments, the interest module uses similar methods similar to those discussed above discussing determining a pattern of consumption and detecting a reduction of interest relating to the determined pattern. In one embodiment, a trained machine learning model determines a pattern of consumption and then determines if there is a change in the pattern of consumption. If the change in the pattern of consumption indicates a reduction of interest, the interest module detects the reduction of interest. For example, if a user went from consuming three hours of content a week to one hour a week, the interest module would detect this as a reduction of interest in the content series. 
     At  706 , the interest module, by control circuitry  404 , identifies a reason for reduction of interest. In some embodiments, the interest module uses an identified change in pattern and contextual information based at least in part on the change in pattern and identifies a reason for the reduction of interest. For example, if the change in the pattern begins at week seven, the interest module can use contextual information to determine that during week seven of the content series a main actor left the program. Accordingly, the interest module identifies the reason for the reduction of interest is the main actor leaving the program. In an embodiment, the interest module can determine that there may be more than one reasons for the reduction of interest. In such an embodiment, the interest module may rank each reason for the reduction of interest with a confidence score, wherein each confidence score indicates the likelihood that the ranked reason is the most influential in the reduction of interest. In an embodiment, the reason or reasons for reduction of interest are sent to a server, where the reasons can be aggregated to produce reduction of interest data. 
     At  708 , the interest module, by control circuitry  404 , causes an operation to be performed based on the reason for the reduction of interest. In some embodiments, the operation includes providing a spoiler, generating an advertisement, offering promotional giveaways, similar such operations, or a combination thereof. For example, if the reason for the reduction of interest was an actor leaving the show and in a future episode the actor returns, the interest module can cause a spoiler to be provided to be displayed on a user device  502  indicating that the actor will return to the show. 
       FIG.  8    is an illustrative flowchart of a process  800  for determining a reduction of interest in a content series and providing a spoiler based on the reduction of interest, in accordance with some embodiments of the disclosure. At step  802 , the interest module, by control circuitry  404 , monitors consumption of a content series. The monitoring of consumption of the content series can use the methods and techniques described in step  602  above. 
     At step  804 , the interest module, by control circuitry  404 , detects a reduction of interest in a content series. The detection of a reduction of interest in the content series can use the methods and techniques described in step  704  above. 
     At step  806 , the interest module, by control circuitry  404 , identifies a reason for the reduction of interest. The identification of the reason for the reduction of interest can use the methods and techniques described in step  706  above. In some embodiments, the interest module will use a user&#39;s interactions with social networking sites to aid in identifying a reason for the reduction of interest. 
     At step  808 , the interest module, by control circuitry  404 , generates or selects and provides spoiler content to be provided based on the reason for the reduction of interest. In some embodiments, the interest module has relational databases or metadata, stored in storage  408 , that allows the interest module to associate pre-generated spoilers with certain identifiers, wherein the identifiers can relate to actors, storylines, and similar such information. In an embodiment, the interest module cross-references the reason for reduction of interest with the identifiers in the database to select a spoiler. In some embodiments, the interest module sends the reason for the reduction of interest to user devices  502 , media content sources  512 , and/or servers  514 , which generate and send one or more spoilers to the interest module. Spoilers are events in a content item that reveal important plot elements that a user does not know and was not intended to know before watching the content. Some examples of spoilers include details of the conclusion of a plot or storyline that the user has not yet watched, plot twists, character deaths, climatic events, and similar such events. In some embodiments, the spoilers can be designated with scores relating to the amount of subject matter included in the spoiler. For example, a spoiler indicating that a character will be featured in a new episode would have a lower score than a spoiler indicating that the character will be featured in a new episode where he dies. 
     In an embodiment, the interest module assigns a score based on the interest module&#39;s determination of a spoiler&#39;s impact on a user. In an embodiment, the interest module can make the determination of a spoiler&#39;s impact by cross-referencing the profile of the user and the reduction of interest to an identifier of the spoiler using techniques described above. In an embodiment, the interest module uses data received from content series producers, critics, trained machine learning models, or similar such resources to influence the determination of the spoiler&#39;s impact score. In some embodiments, the impact scores may be unique to individuals or the impact score may apply generally. For example, a spoiler related to a major plot twist will have a generally high impact score regardless of the user, but a spoiler&#39;s impact score related to a particular actor may vary depending on the user. In some embodiments, the interest module will determine a low impact score for a spoiler including an actor when a user does not care about that actor. In an embodiment, where a user likes a particular actor, the interest module will determine a high impact score for spoilers including that actor. In some embodiments, the interest module generates a spoiler based on the reason for the reduction of interest. In an embodiment, if the interest module determines that the reason for the reduction of interest is that the actor Mandy Patinkin left the content series, Criminal Minds, the interest module will generate a spoiler in the form of a commercial indicating that Mandy Patinkin will return to the show. The interest module causes the user device  400  to display the commercial with the spoiler on the display  412  of user device  400 . 
     At step  810 , the interest module, by control circuitry  404 , determines if the spoiler sufficiently increased the user&#39;s interest. In some embodiments, the interest module uses methods similar to those discussed above to determine if the spoiler sufficiently increased interest. In one embodiment, a trained machine learning model determines a pattern of consumption and then determines if there is a change in the pattern of consumption corresponding to providing the spoiler. If the change in the pattern of consumption indicates an increased interest, the interest module does not generate any additional spoilers and the process  800  ends. In an embodiment, if there is no change in the pattern of consumption corresponding to providing the spoiler, or if there is an increased interest but the increased interest is below a threshold, the interest module returns to step  808  and generates or selects an additional spoiler. In one embodiment, the interest module generates a second spoiler comprising additional subject matter. In an embodiment, the interest module generates a second spoiler with a spoiler score greater than the spoiler score of the first displayed spoiler. For example, the first spoiler may indicate that a character will be featured in a new episode; after no increased interest, the second generated spoiler will indicate that the character will be featured in a new episode where he dies. In an embodiment, the interest module provides the second spoiler with a different method than that of the first spoiler. For example, if the first spoiler was a notification on a mobile device as in  FIG.  3 A , the second spoiler may be a commercial shown on a different device as shown in  FIG.  3 B . 
     It is contemplated that some suitable steps or suitable descriptions of  FIGS.  6 - 8    may be used with other suitable embodiments of this disclosure. In addition, some suitable steps and descriptions described in relation to  FIGS.  6 - 8    may be implemented in alternative orders or in parallel to further the purposes of this disclosure. For example, some suitable steps may be performed in any order or in parallel or substantially simultaneously to reduce lag or increase the speed of the system or method. Some suitable steps may also be skipped or omitted from the process. Furthermore, it should be noted that some suitable devices or equipment discussed in relation to  FIGS.  1 - 5    could be used to perform one or more of the steps in  FIGS.  6 - 8   . 
     The processes discussed above are intended to be illustrative and not limiting. One skilled in the art would appreciate that the steps of the processes discussed herein may be omitted, modified, combined, and/or rearranged, and any additional steps may be performed without departing from the scope of the invention. More generally, the above disclosure is meant to be exemplary and not limiting. Only the claims that follow are meant to set bounds as to what the present invention includes. Furthermore, it should be noted that the features and limitations described in any one embodiment may be applied to any other embodiment herein, and flowcharts or examples relating to one embodiment may be combined with any other embodiment in a suitable manner, done in different orders, or done in parallel. In addition, the systems and methods described herein may be performed in real time. It should also be noted that the systems and/or methods described above may be applied to, or used in accordance with, other systems and/or methods.