Distribution of promotional content based on reaction capture

Embodiments describe an approach for improving user consumption and promotion of multi-media content. Embodiments retrieve social media and location-specific context information from one or more social media platforms associated with one or more audience members. Embodiments determine a baseline mood for the one or more audience members based on the one or more audience member's social media activity and social media posts. Embodiments of capture one or more images of one or more audience member reactions while the one or more audience members view the multi-media content. Embodiments identify relevant scenes in the multi-media content based on the captured one or more audience member reactions. Additionally, embodiments generate promotional content based on the identified relevant scenes; and distribute the promotional content on the one or more social media platforms.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of multi-media promotions and audience reaction, and more particularly to improving multi-media and graphic content promotions based on an audience's reaction to multi-media.

Promotions or advertisements of any multi-media and/or graphics content, known in the art, plays an active role in sustained and improved consumption. Promotions mostly come in the form of summaries, highlights, trailers, promotional events, advertisements, and social media posts. The content of the promotions will mostly be decided by one or more editors of the multi-media and/or graphics content. The highlights and trailers generated by the editors or production company are based on the interests and/or perceived interests of the editor or production company and what the editor or production company perceives as the most interesting summary points for consumers.

SUMMARY

Embodiments of the present invention disclose a method, a computer program product, and a system for improving promotion and user consumption of multi-media content, the embodiments comprising: retrieving, by one or more processors, social media and location-specific context information from one or more social media platforms associated with one or more audience members; determining, by the one or more processors, a baseline mood for the one or more audience members based on the one or more audience member's social media activity and social media posts; capturing, by the one or more processors, one or more images of one or more audience member reactions while the one or more audience members view the multi-media content; identifying, by the one or more processors, relevant scenes in the multi-media content based on the captured one or more audience member reactions; generating, by the one or more processors, promotional content based on the identified relevant scenes; and distributing, by the one or more processors, the promotional content on the one or more social media platforms.

DETAILED DESCRIPTION

Consumers may or may not enjoy the generated promotional content distributed by media companies. To better attract the audience to multi-media or graphics content, the promotional contents may be selectively chosen based on an audience's mood-based reaction and/or social media posts.

Embodiments of the present invention can record and process upcoming or ongoing multi-media content release information. Multi-media content can be, but is not limited to, a movie, TV show, music video, video game, and/or any other form of multi-media known in the art. Multi-media content release can be, but is not limited to, a movie premiere, TV show premiere, music video premiere, a streaming release, and/or any other type of multi-media release known in the art. Multi-media content release information can be, but is not limited to, a release date, premiere date, location of the premiere, number of premiere guests, time of the premiere and/or release, etc.

Additionally, embodiments of the present invention can record and process the multi-media release's logistical and ontological attributes. Embodiments of the present invention can comprise a cloud-based application programming interface (API) that can retrieve social media information and location-specific context information of a set of select audience members viewing the multi-media content to define a baseline of the audience prior to a multi-media content release. An audience member can be, but is not limited to, a guest of a premiere, a celebrity, an online user, a social media influencer, any other person viewing a multi-media content release, and/or any other person known in the art. Social media information can be, but is not limited to, one or more audience member social media posts, likes, interests, friends/followers (e.g., amount/number of friend and followers the user has), who the set of select audience members is following, amount of people the one or more audience members are following, social media interaction (e.g., comments and/or replies), and/or any other form of social media information known in the art.

Location-specific context information is any contextual location information known in the art. For example, a user checking into a place or location, as reflected in their social profile, a place where the user is going to or has previously visited, as reflected in their social profile, an event the user has attended, as reflected in the user's social profile, or an emotional state or a mood the user is undergoing or has previously experienced, as reflected in their social profile. Embodiments of the present invention can comprise low light cameras that can capture images of the audience members in attendances. Embodiments of the present invention can further comprise an image processing system that can recognize expressions from images (e.g., captured images of audience members). In some embodiments, an audience member can be a social media user that has a high social media influence. High social media influence is determined based on an exceeded predetermined number of followers, social media posts, likes generated, comments generated and/or shared social media posts. For example, if an audience member has over three million followers and generates over 30,000 likes per post, then the audience member is determined to have a high social media influence.

Embodiments of the present invention can provide a baseline and/or preset of the mood of an audience prior to a multi-media release. Embodiments of the present invention can capture images of the audience members while the multi-media content is playing. Embodiments of the present invention can extract the mood changes and expressions of the audience members during the course of a multi-media premiere and/or while the multi-media content is streaming (e.g., playing). Embodiments of the present invention can identify the most relevant and/or sensational scenes within the multi-media content based on the audience's captured mood changes. Embodiments of the present invention can generate promotional content, based on the identified relevant and/or sensational scenes, by extracting and collating the most relevant and/or sensational scenes from the multi-media content to generate interest in viewers to see the full content (e.g., generate a movie trailer).

Embodiments of the present invention can identify the relevant scenes, songs, and/or moments in multi-media content that were widely enjoyed by audience members (i.e., guests of a movie premiere). Embodiments of the present invention can identify the scenes that were poorly received by the audience members and recommend the poorly received scenes for editing, so that during the transmission of the multi-media content, a streaming service or broadcaster can make sure the prime scenes are highlighted. Embodiments of the present invention can rate the identified scenes, songs, and moments in the multi-media content and compare the identified scenes, songs, and/or moments to any other movies on one or more rating sites.

Embodiments of the present invention can identify video contents (e.g., relevant scenes and/or levels) extracted out of video games based on different emotions expressed by one or more audience members (i.e., users). The identified video contents can help video game manufacturers realize and understand how different levels are affecting the users and how involved the users are in the game, levels, and/or characters. The extracted information can help the user improve the way video game manufacturers develop video games by adding similar levels and/or characters based on the levels and/or characters the users enjoyed and suppressing the levels and/or characters the users didn't enjoy. It is known in the art that a healthy comparison of video games is enabled by user engagements, which in turn enables improvements in the field of video game development. Additionally, the enabled improvement described above for video games applies to movies, mobile applications, and/or any multi-media content known in the art.

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'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).

Service Models are as Follows:

Deployment Models are as Follows:

FIG. 3is a functional block diagram illustrating distributed data processing environment300, in accordance with an embodiment of the present invention. Distributed data processing environment300, can be a cloud computing environment similar to environment50inFIG. 1, and includes computing device310, multimedia and graphics highlight management component (MGHMC)312, API340, and server computer320interconnected over network330. In various embodiments, not depicted inFIG. 3, environment300can include one or more cameras, in which the one or more cameras are low light camera and the one or more cameras are in communication with computing device310, API340, and server computer320, via network330.FIG. 3provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made by those skilled in the art without departing from the scope of the invention as recited by the claims.

Network330can be, for example, a storage area network (SAN), a telecommunications network, a local area network (LAN), a wide area network (WAN), such as the Internet, a wireless technology for exchanging data over short distances (using short-wavelength ultra-high frequency (UHF) radio waves in the industrial, scientific and medical (ISM) band from 2.4 to 2.485 GHz from fixed and mobile devices, and building personal area networks (PANs) or a combination of the three), and can include wired, wireless, or fiber optic connections. Network330can include one or more wired and/or wireless networks that can receive and transmit data, voice, and/or video signals, including multimedia signals that include voice, data, text and/or video information. In general, network330can be any combination of connections and protocols that will support communications between computing device310, API340, and server computer320, and any other computing devices and/or storage devices (not shown inFIG. 3) within distributed data processing environment300.

In various embodiments, computing device310can be, but is not limited to, a standalone device, a client, a server, a laptop computer, a tablet computer, a netbook computer, a personal computer (PC), a smart phone, a desktop computer, a smart television, a smart watch, a radio, a stereo system, a cloud based service (e.g., a cognitive cloud based service), and/or any programmable electronic computing device capable of communicating with various components and devices within distributed data processing environment300, via network330or any combination therein. In general, computing device310can be representative of any programmable computing device or a combination of programmable computing devices capable of executing machine-readable program instructions and communicating with users of other computing devices via network330and/or capable of executing machine-readable program instructions and communicating with server computer320.

In other embodiments, computing device310can represent any programmable electronic computing device or combination of programmable electronic computing devices capable of executing machine readable program instructions, manipulating executable machine-readable instructions, and communicating with server computer320and other computing devices (not shown) within distributed data processing environment300via a network, such as network330. Computing device310can include an instance of user interface (UI)306, local storage308, and/or MGHMC312. In various embodiments, not depicted inFIG. 3, computing device310can comprise one or more cameras. In various embodiments, the one or more cameras can be low light cameras. In other embodiments, the one or more cameras can be any type of camera known in the art. In various embodiments, not depicted inFIG. 3, computing device310can have a plurality of user interfaces. In other embodiments, not depicted inFIG. 3, distributed data processing environment300can comprise a plurality of computing devices, plurality of server computers, and/or one a plurality of networks. Computing device310can include internal and external hardware components, as depicted, and described in further detail with respect toFIG. 5.

User interface (UI)306provides an interface to MGHMC312on computing device310. Computing device310, via user interface306, can enable a user and/or a client to interact with MGHMC312and/or server computer320in various ways, such as sending program instructions, receiving program instructions, sending and/or receiving messages, updating data, sending data, inputting data, editing data, collecting data, and/or receiving data. In one embodiment, UI306can be a graphical user interface (GUI) or a web user interface (WUI) and can display at least text, documents, web browser windows, user options, application interfaces, and instructions for operation. UI306can include information (such as graphic, text, and sound) presented to a user and control sequences the user employs to control operations. In another embodiment, UI306can be a mobile application software providing an interface between a user of computing device310and server computer320. Mobile application software, or an “app,” can be designed to run on smart phones, tablet computers and other computing devices. In an embodiment, UI306can enable the user of computing device310to at least send data, input data, edit data (annotations), collect data and/or receive data.

Server computer320can be a standalone computing device, a management server, a web server, a mobile computing device, one or more client servers, or any other electronic device or computing system capable of receiving, sending, and processing data. In other embodiments, server computer320can represent a server computing system utilizing multiple computers such as, but not limited to, a server system, such as in a cloud computing environment. In another embodiment, server computer320can represent a computing system utilizing clustered computers and components (e.g., database server computers, application server computers, etc.) that act as a single pool of seamless resources when accessed within distributed data processing environment300. Server computer320can include internal and external hardware components, as depicted, and described in further detail with respect toFIG. 5.

Each of shared storage324and local storage308can be a data/knowledge repository and/or a database that can be written and/or read by one or a combination of MGHMC312, server computer320and computing device310. In the depicted embodiment, shared storage324resides on server computer320and local storage308resides on computing device310. In another embodiment, shared storage324and/or local storage308can reside elsewhere within distributed data processing environment300, provided that each can access and is accessible by computing device310and server computer320. Shared storage324and/or local storage308can each be implemented with any type of storage device capable of storing data and configuration files that can be accessed and utilized by server computer320, such as, but not limited to, a database server, a hard disk drive, or a flash memory.

In other embodiments, shared storage324and/or local storage308can each be a hard drive, a memory card, a computer output to laser disc (cold storage), and/or any form of data storage known in the art. In some embodiments, shared storage324and/or local storage308can each be one or more cloud storage systems and/or databases linked to a cloud network. In various embodiments, shared storage324can access, store, and/or house multi-media content, relevant scene information, social media information, audience member data, and/or data shared throughout distributed data processing environment300(e.g., shared data memory).

API340can be, for example, a Platform-as-a-Service (PaaS) API, a Software-as-a Service (SaaS) API, an Infrastructure-as-a-Service (IaaS) API, a cloud provider and cross-platform API, a representational state transfer (REST) API, a simple object access protocol (SOAP) API, or any API known in the art. In various embodiments, not depicted inFIG. 3, API340can be located and/or executed on server computer320, computing device310, and/or anywhere within distributed data processing environment300as long as API340is interconnected to computing device310, server computer320, and/or MGHMC312via network330. In various embodiments, API340can monitor and capture historic and current social media and location-specific context information from one or more public social media accounts of audience members (e.g., celebrities, professional gamers, and/or social media influencers) invited to attend a multi-media release (e.g., a movie premiere).

In some embodiments, API340can monitor and capture historic and current social media activity and location-specific context information from one or more public social media accounts of celebrities, professional gamers, and/or social media influencers (e.g., audience members) to determine the personality traits, and/or moods of the one or more audience members. In various embodiments, API340can determine the mood and/or emotion of an audience member by analyzing a writing style from the audience member's social media posts via a tone analyzer. In various embodiments, API340can send the determined mood and/or emotion of the audience member to MGHMC312, providing MGHMC312the capability to preset one or more audience members' moods (e.g., create a baseline mood for a plurality of audience members).

In various embodiments, MGHMC312is executed on computing device310. In other embodiments, MGHMC312can be executed on server computer320. In some embodiments, MGHMC312can be located and/or executed anywhere within distributed data processing environment300. In various embodiments, MGHMC312can be connected to and communicates with a cloud-based API (e.g., API340). In various embodiments, MGHMC312can store multi-media content and the multi-media content's related information. Multi-media content related information can be, but is not limited to, a movie genre, length of the movie (e.g., runtime), number of scenes, types of characters, tracked and timed scenes (e.g., funny scene at 05:26, sad scene at 25:01, etc.), movie rating, technical details, story outline, script, video game genre, type of video game (e.g., racing, action/adventure, etc.), video game rating, and/or any other related information known in the art.

In one example, for movies, MGHMC312saves the multi-media content related information, such as the upcoming premiere showing including the premiere show's genre, technical details, and story outline. In another example, for video games, MGHMC312saves the different game levels and other technical details as the multi-media content related information. In a different example, for graphic rich software applications, MGHMC312records different parameters of an application usage such as mapping between different frames and views of the application and the corresponding mood variation and/or reaction of the user, loading time, time spent on each frame, cancelled usage of dialogue boxes, incorrect usage of dialogue boxes, and unresponsiveness as multi-media content related information.

In various embodiments, MGHMC312can assign seating arrangements to invited audience members and manage the seating positions of one or more audience members in an arena. In various embodiments, MGHMC312can arrange or assign audience member seating based on individual audience member baseline moods to improve cluster accuracy. For example, MGHMC312assigns audience members whose baseline mood is happy or similar to seat together so a clusters mood is preestablished and can be accurately tracked. In some embodiments, MGHMC312can seat audience members who have complete opposite baseline moods next to each other. In various embodiments, MGHMC312can assign audience members to a seat in order to accurately track mood and facial expression changes of one or more particular audience members.

In various embodiments, MGHMC312can generate and manage a guest list for an event. In various embodiments, MGHMC312can use the determined personality traits from social media analysis, and/or established moods (e.g., baseline mood) of the one or more audience members to determine which audience member's personality traits and/or mood best matches the multi-media content related information of the movie (e.g., type of movie and genre of the movie). For example, if the audience member's baseline mood is happy, a user (e.g., a marketing director) might want to gauge how deep of a reaction the movie can obtain; therefore, MGHMC312can assign the audience member to a sad movie. In various embodiments, MGHMC312can send invitations to persons listed on the guest list. In various embodiments, MGHMC312can store audience responses that were captured during a show or event.

In various embodiments, MGHMC312can continuously capturing one or more audience members' reactions from the time the multi-media content begins playing until the multi-media content ends (e.g., stops playing). In various embodiments, not depicted inFIG. 3, MGHMC312can capture the reactions of one or more audience members via low light capturing cameras, wherein the low light cameras are attached to the head rest on the seat in front of the audience members. In another embodiment, low light cameras can be positioned throughout the venue where a premier or multi-media release is occurring, in which each camera is designated to monitor and capture the mood for a portion of the audience. For example, a first camera monitors and captures the emotions/moods from audience members in seats 1-5 in rows 1-3, a second camera monitors and captures the emotions/moods from audience members in seats 6-10 in rows 1-3, etc. In some embodiments, the low light cameras are positioned in the rafters or on cables above the audience. In various embodiments, MGHMC312can send the captured images of the audience to a facial expression analyzer, not depicted inFIG. 3.

In some embodiments, not depicted inFIG. 3, the facial expression analyzer can be a component of MGHMC312. In various embodiments, the facial expression analyzer can analyze the captured images for movements in the audience's eyebrows, mouth, eyes, and/or other facial movements known in the art. In various embodiments, the facial expression analyzer can identify the expression of one or more persons (e.g., guests/audience members) from the captured images based on preset training samples of facial expressions and emotions. Additionally, in various embodiments, the facial expression analyzer can identify the expression of one or more persons (e.g., guests/audience members) from the captured images through one or more emotion APIs, not depicted inFIG. 3, that can detect emotions like anger, fear, joy, neutral, sadness, surprise, and/or any other emotion known in the art. In various embodiments, the facial expression analyzer can map the identified facial expressions against the time lapsed in the played multi-media content (e.g., movie) to determine which scene causes one or more audience members to react/express emotion (e.g., smile, laugh, cry, etc.).

In various embodiments, MGHMC312can receive responses from multi-modal sensors, not depicted inFIG. 3, for the analysis of visual features, acoustic features, linguistic features, and physical features from multi-media content with the help of machine learning algorithms to extract an emotional state out of each level or a scene of a game. In one particular embodiment, MGHMC312can continuously capture the emotional state of one or more levels or scenes of a game, via multi-modal sensors. Additionally, in this particular embodiment, MGHMC312can continuously capture one or more user's and/or one or more audience member's reactions while the one or more users play the video game. Additionally, in this particular embodiment, MGHMC312can analyze the captured emotional state of the one or more levels or scenes against the captured reactions of the one or more users and/or one or more audience members. In this particular embodiment, MGHMC312can store and process the captured scenes and game levels. In other embodiments, MGHMC312can capture a stadium of audience members' reactions while the audience members watch a video game tournament, video game play, and/or a release trailer of the video game.

In various embodiments, MGHMC312can group audience response to form clusters, in which one or more clusters comprise audience members who share similar baseline moods. In various embodiments, MGHMC312can input and/or send the one or more clusters to processing subsystem314. In depicted embodiment, processing subsystem314is a component of MGHMC312. In other embodiments, processing subsystem314can be located anywhere within distributed data processing environment300as long as subsystem314is interconnected to computing device310, server computer320, and/or MGHMC312via network330. In various embodiments, processing subsystem314can continuously identify the facial expression and continuously determine the mood of one or more audience members from the one or more clusters while multi-media content is playing. In various embodiments, processing subsystem314can plot the determined audience member moods against playing multi-media content (e.g., match the captured reaction and/or determined mood to scenes in the played multi-media content).

In various embodiments, processing subsystem314can weigh the captured emotions based on one or more audience members who shared the same or substantially similar moods during a particular scene or part of played multi-media content. In various embodiments, processing subsystem314can map one or more audience members in one or more clusters to one or more emotions against time. In various embodiments, processing subsystem314can comprise one or more cognitive APIs, in which the one or more cognitive APIs can understand emotions based on captured visuals (e.g. images and/or video), audio analysis, and scene composition analysis. In various embodiments, the one or more cognitive APIs identify one or more scenes from the played multi-media content and map the identified one or more scenes against time to generate a time map of one or more scenes in the multi-media content.

In various embodiments, processing subsystem314can map the identified scenes with the one or more recorded audience members responses against one or more clusters with one or more scenes being given weight against the one or more recorded audience members responses in each cluster. In one particular embodiment, the largest cluster getting mapped to a particular scene can identify the actual audience members experience and/or reaction to the particular scene. In various embodiments, processing subsystem314can send the recorded audience members experience, reactions and/or moods to MGHMC312. In various embodiments, MGHMC312can output relevant scenes based on the need to improve the audience member experience. Relevant scenes identified from the multi-media content can be used for promotional events and advertising as well as collecting feedback. In various embodiments, the parts/scenes of the multi-media content that are not impacting (e.g., do not invoke mood or reaction changes from the audience members) can be recommended to one or more editors and/or producers to be removed from the multi-media content. In various embodiments, MGHMC312determines relevant scenes in multi-media content by tracking the movement of audience members between clusters for one or more scenes while the multi-media content is playing. In this particular embodiment, MGHMC312can map audience members' movement between clusters to identify which scenes invoke the audience members' reaction/mood.

In various embodiments, the processing subsystem can receive emotional data from an audience member's baseline and/or captured audience member reactions and/or moods throughout the played multi-media content (e.g., movie premiere) to form clusters of audience members. For example, the processing subsystem receives one or more audience members baseline moods and forms clusters of audience members, in which the clusters group audience members based on a common age group, and shared common moods against one or more scenes or levels. In various embodiments, one or more clusters can begin with audience members belonging to similar age groups and common baseline moods. For example, the processing subsystem generates clusters for a new family movie based on an audience's predetermined baseline moods. In this particular example, the clusters are depicted as:

In this particular example, as the movie progresses, content from the movie is mapped to the clusters against time as shown below:

In various embodiments, one or more scenes can be identified and labeled by MGHMC312, via scene composition analysis. For example, while MGHMC312conducts a scene composition analysis of a family movie, MGHMC312identifies a funny scene at time:“00:15” (i.e., minute 15), a sad scene at time:“00:45”, etc. In various embodiments, MGHMC312can map the identified and labeled scenes to the one or more audience members' identified reactions. In various embodiments, MGHMC312can evaluate the percentage increase in one or more clusters to determine the overall audience experience against the played multi-media content at any given time during the event. In various embodiments, MGHMC312can assign the highest positive ranking to the cluster that has a superlative emotion in relation to the identified emotion of a particular scene and the lowest ranking to the cluster representing the emotion that overlaps with the emotion identified in a particular scene.

For example, to measure the effectiveness of a happy scene, MGHMC312takes the initial clusters, demonstrated below:

In this particular example, MGHMC312takes all the positive emotion clusters (i.e., joyful, happy, etc.) in the generated clusters including the neutral cluster and ranks the clusters based on the tone of the cluster emotion towards the emotion of a particular scene which is ‘happy’. In this particular example, joyful being a superlative word for happy, the joyful cluster gets the highest ranking. In this particular example, the happy cluster being the same mood as the mood in the identified scene gets a normal ranking and the neutral cluster gets the lowest ranking. In various embodiments, MGHMC312can determine the overall effectiveness (e.g., relevant scenes) of one or more scenes by analyzing the movement of audience members between clusters. For example, MGHMC312analyzes the percentage of audience members that have moved from a negative or neutral cluster to a positive cluster. In the particular example, the percentage of audience members that moved from negative clusters to positive clusters are added to the respective weights of positive clusters to determine the overall effectiveness of a scene. In various embodiments, if the movement or concentration of audience members for a particular scene is above a predetermined threshold, then MGHMC312can identify that particular scene as a relevant scene.

In one particular example, for a movie, MGHMC312schedules the premiere showing and manages the audience members by sending invites based on a multi-media producer's request. For example, a movie producer requests MGHMC312to send the location and time of the movie premiere to a guest list input by the movie producer. In this particular example, MGHMC312identifies the seating positions of every audience member inside the theatre, and, via API340, captures celebrities (i.e., audience members) social media activity to create a baseline mood for every audience member in attendance. In this particular example, once the movie begins, MGHMC312can capture audience members' facial expressions while the movie is playing, via low light capturing cameras. In this particular example, MGHMC312identifies the audience members mood and expression associated with all scenes by analyzing the captured facial expressions, via processing subsystem314. Additionally, in this particular example, MGHMC312tracks the audience members' reaction to scenes in the film and identifies relevant scenes by identifying which scenes cause the most audience members to react.

In one particular embodiment, for a video game, MGHMC312can identify a registered user, via facial recognition or any other form of biometric security known in the art, not depicted inFIG. 3. In other embodiments, if MGHMC312is unable to identify a user, then the user can be identified as anonymous. Continuing the particular embodiment above, API340can capture the social media activity of the user to establish the user's baseline mood. In this particular embodiment, once the video game begins, MGHMC312, can retrieve and/or receive responses from the multi modal sensors analyze the visual features, acoustic features, linguistic features and physical features of the game, via machine learning algorithms, to extract an emotional state out of each level or a scene of the video game. In this particular embodiment, MGHMC312can continuously capture the user's reaction to the video game while the user plays. In this particular embodiment, MGHMC312can identify and output game levels and/or scenes that cause the users to react and/or exceed a predetermined threshold. For example, if the level causes the user to express excitement or amazement, then MGHMC312can identify these levels as relevant levels. In various embodiments, the continuously captured user reactions can be tracked and/or analyzed by processing subsystem314.

In one particular embodiment, for a website/mobile application, MGHMC312can identify a registered user, via facial recognition or any other form of biometric security known in the art, not depicted inFIG. 3. In other embodiments, if MGHMC312is unable to identify a user, then the user can be identified as anonymous. Continuing the particular embodiment above, API340can capture the social media activity of the user to establish the user's baseline mood. In this particular embodiment, once the interaction between the user and the website (e.g., application) begins, MGHMC312can retrieve and/or receive responses and captured images from the multi modal sensors and web-cameras to analyze the visual features, acoustic features, linguistic features, other parameters such as time spent on one or more frames, used/cancelled/reworked frames, and physical features of the website, via machine learning algorithms commonly known in the art, to extract an emotional state out of one or more levels or scenes of the website.

In this particular embodiment, MGHMC312can continuously capture the user's reaction to the website while the user interacts with the website. In this particular embodiment, MGHMC312can identify scenes and/or images from the website that cause the user to react and/or exceed a predetermined threshold. For example, if a scene causes the user to express excitement or amazement then MGHMC312can identify that scene as a relevant scene. In various embodiments, the continuously captured user reactions can be tracked and/or analyzed by processing subsystem314. In various embodiments, MGHMC312can output, retrieve, and/or receive a survey associated with the website/mobile application to generate a summary of insights. In various embodiments, MGHMC312can collect the summary of insights to generate a net promoter score (NPS) for the website/mobile application. In various embodiments, MGHMC312can generate a custom survey based on the identified relevant scenes and/or characteristics of the website/mobile application.

FIG. 4illustrates operational steps of MGHMC312, generally designated400, in communication with server computer320within distributed data processing environment300ofFIG. 3, for improving user consumption of multi-media content, in accordance with an embodiment of the present invention.FIG. 4provides an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made by those skilled in the art without departing from the scope of the invention as recited by the claims.

In step402, MGHMC312records media release information. In various embodiments, MGHMC312can receive and/or record one or more media release information. In some embodiments, MGHMC312can generate a guest list for an event for the media release event. In various embodiments, MGHMC312can manage and send invitations to the guest list. In various embodiments, MGHMC312can arrange the audience member seating order and book the venue for the media release event.

In step404, MGHMC312retrieves social media and location-specific context information. In various embodiments, MGHMC312can monitor and retrieve social media and location-specific context information of one or more audience members attending a media release event from one or more social media platforms.

In step406, MGHMC312determines a baseline mood for the audience members. In various embodiments, MGHMC312can determine a baseline mood for the one or more audience members based on the retrieved social media and location-specific context information. For example, MGHMC312determines a baseline mood for each audience member attending a movie premier based on each audience members social media posts/activity.

In step408, MGHMC312presets the mood for the audience members. In various embodiments, MGHMC312can preset the mood for one or more audience members in attendance based on the determined baseline mood of the one or more audience members. In various embodiments, presetting an audience member's mood can comprise generating mood clusters and assigning the audience member to a cluster based on the audience member's determined baseline mood.

In step410, MGHMC312captures images of the audience member's reactions. In various embodiments, MGHMC312can continuously capture images of the audience member's reactions to the multi-media content while the multi-media content is playing. For example, continuously capturing an audience member's reaction while the movie is playing. In various embodiments, MGHMC312can track the audience member's reactions and movement between clusters.

In step412, MGHMC312identifies relevant scenes. In various embodiments, MGHMC312can identify one or more relevant scenes in multi-media content based on the tracking of audience member reactions and movement between clusters and a predetermined concentration of audience members in desired clusters. For example, if the screened movie is a comedy and a scene causes audience member reactions to move from neutral or negative clusters to positive clusters (e.g., happy, laughing, smiling, joyful, etc.) then the scene that caused the positive reaction is a relevant scene.

In step414, MGHMC312generates promotional content. In various embodiments, MGHMC312can generate promotion content based on the one or more identified relevant scenes in a multi-media content. In some embodiments, MGHMC312can generate promotional videos and/or movie trailers for a movie.

In step416, MGHMC312distributes the promotional content. In various embodiments, MGHMC312can send the promotional content to the audience members that attended a screening event and instruct them to post the promotional content on their social media account. In other embodiments, MGHMC312can send and distribute the promotional content to producers, movie theatres, streaming services, television networks, social media influencers, social media service company, social networking service company, and/or internet providers.

FIG. 5depicts computer system500, where server computer320represents an example of computer system500that includes OC312. The computer system includes processors501, cache503, memory502, persistent storage505, communications unit507, input/output (I/O) interface(s)506, display509, external device(s)508and communications fabric504. Communications fabric504provides communications between cache503, memory502, persistent storage505, communications unit507, and input/output (I/O) interface(s)506. Communications fabric504can be implemented with any architecture designed for passing data and/or control information between processors (such as microprocessors, communications, and network processors, etc.), system memory, peripheral devices, and any other hardware components within a system. For example, communications fabric504can be implemented with one or more buses or a crossbar switch.

Memory502and persistent storage505are computer readable storage media. In this embodiment, memory502includes random access memory (RAM). In general, memory502can include any suitable volatile or non-volatile computer readable storage media. Cache503is a fast memory that enhances the performance of processors501by holding recently accessed data, and data near recently accessed data, from memory502.

Program instructions and data used to practice embodiments of the present invention can be stored in persistent storage505and in memory502for execution by one or more of the respective processors501via cache503. In an embodiment, persistent storage505includes a magnetic hard disk drive. Alternatively, or in addition to a magnetic hard disk drive, persistent storage505can include a solid-state hard drive, a semiconductor storage device, read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, or any other computer readable storage media that is capable of storing program instructions or digital information.

The media used by persistent storage505can also be removable. For example, a removable hard drive can be used for persistent storage505. Other examples include optical and magnetic disks, thumb drives, and smart cards that are inserted into a drive for transfer onto another computer readable storage medium that is also part of persistent storage505.

Communications unit507, in these examples, provides for communications with other data processing systems or devices. In these examples, communications unit507includes one or more network interface cards. Communications unit507can provide communications through the use of either or both physical and wireless communications links. Program instructions and data used to practice embodiments of the present invention can be downloaded to persistent storage505through communications unit507.

I/O interface(s)506enables for input and output of data with other devices that can be connected to each computer system. For example, I/O interface506can provide a connection to external devices508such as a keyboard, keypad, a touch screen, and/or some other suitable input device. External devices508can also include portable computer readable storage media such as, for example, thumb drives, portable optical or magnetic disks, and memory cards. Software and data used to practice embodiments of the present invention can be stored on such portable computer readable storage media and can be loaded onto persistent storage505via I/O interface(s)506. I/O interface(s)506also connect to display509.

Display509provides a mechanism to display data to a user and can be, for example, a computer monitor.