In one aspect, a method includes using a Wi-Fi module of a computing device to detect that an end-user has exited a monitored environment of the computing device, where the monitored environment comprises an Internet of Things (IoT) device communicatively coupled to the computing device over an IoT network. The method also involves in response to detecting that the end-user has exited the monitored environment, triggering a first IoT action at the IoT device. The method also involves after detecting that the end-user has exited the monitored environment and triggering the first IoT action, using the Wi-Fi module of the computing device to detect that the end-user has reentered the monitored environment. The method also involves in response to detecting that the end-user has reentered the monitored environment, triggering a second IoT action at the IoT device.
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
G11B 27/00 - EditingIndexingAddressingTiming or synchronisingMonitoringMeasuring tape travel
H04N 21/433 - Content storage operation, e.g. storage operation in response to a pause request or caching operations
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for demographic predictions for content items. An example embodiment operates by assigning weights representing demographics to a first plurality of nodes of a predictive model and assigning predictive values representing predicted demographics to a second plurality of nodes of the model. Pairwise distances between the predictive values for the nodes of the second plurality of nodes and the weighted values of the first plurality of nodes may be calculated and the shortest calculated pairwise distances may be used to assign demographics for content items corresponding to nodes of the first plurality of nodes to content items corresponding nodes of the second plurality of nodes. When content is requested, a content item for which the same demographic has been assigned may be recommended to the requestor.
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
3.
MULTIMEDIA FORMATS FOR MULTIPLE DISPLAY AREAS IN A DISPLAY DEVICE
Disclosed herein are system, apparatus, method and/or computer program product embodiments for a multimedia environment that includes a computing device to display multiple multimedia segments within multiple display areas of a display device. The display device can include a first display area to display a first multimedia segment, and a second display area to display a second multimedia segment, which may be an advertisement related to the first multimedia segment. In some embodiments, to fit into the first display area, the first multimedia segment may be in a first format when received, and converted into a second format to fit into the first display area before being displayed in the first display area.
H04N 21/4402 - Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving reformatting operations of video signals for household redistribution, storage or real-time display
H04N 21/431 - Generation of visual interfacesContent or additional data rendering
4.
INTELLIGENT EVENT-BASED PRIORITIZATION AND DISPLAY OF MULTIPLE VIDEO STREAMING DATA
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for providing event-based prioritization and display of a plurality of video streams within a monitoring system. An example aspect operates by receiving continuous event stream which can include video streaming data from multiple video monitoring devices. The method includes identifying events in the streaming data and determining a prioritization for each of the detected events. The identification and prioritization of detected events may be further personalized based on user information. The method may then display the video streams within a visualization interface based on the detected events and prioritization.
G06V 20/40 - ScenesScene-specific elements in video content
G06F 3/04817 - Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
G06V 20/52 - Surveillance or monitoring of activities, e.g. for recognising suspicious objects
H04N 21/431 - Generation of visual interfacesContent or additional data rendering
Disclosed are system, method and/or computer program products for generating a map for map-based management of a plurality of Internet of Things (IoT) devices. An embodiment determines a location of a user within a map in which each IoT device of a plurality of IoT devices is assigned a corresponding map location, generates a list of two or more of the plurality of IoT devices based on the relative position of the user with respect to the locations of the two or more of the plurality of IoT devices, and provides the map and the list to a user interface of an application that enables map-based management of the plurality of IoT devices.
Disclosed herein are system, method, and computer program product embodiments for the detection of human presence in front of a plurality of sensors such as those of speakers and a device with a processor, such as a television. Data gathered from the plurality of sensors may be analyzed by the processor to determine if one or more humans are present proximate to the device. Based on the determined presence or absence of one or more humans, further actions including, inter alia, activating a sleep mode for the one or more humans, shutting off the device in a green mode, or alerting an owner-user to the presence of an intruder can be taken.
H04Q 9/00 - Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
Disclosed herein are system, method, and computer program product embodiments for a port-connected television upgrader system. An embodiment operates by receiving a fetch command from a first instance of an application executing locally on a host device physically connected to a media device through a port of the media device. The fetch command is provided to the media device executing a second instance of the application to fetch a file associated with displaying an interface of the application on the media device. Metadata corresponding to the file that was retrieved by the media device is received. A rendering command corresponding to the interface is determined and provided to the media device that is configured to display the interface of the application responsive to executing the rendering command.
H04N 21/458 - Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming streamUpdating operations, e.g. for OS modules
G06F 13/42 - Bus transfer protocol, e.g. handshakeSynchronisation
H04N 21/431 - Generation of visual interfacesContent or additional data rendering
H04N 21/443 - OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies or resolving scheduling conflicts
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating trailers (previews) for multimedia content. An example aspect operates by generating an initial set of candidate points to generate a trailer for a media content; determining conversion data for each of the initial set of candidate points; determining an updated set of candidate points based on the conversion data; determining an estimated mean and upper bound for each of the updated set of candidate points; computing a value for each of the updated set of candidate points; generating a ranked list based on the value computed for each of the updated set of candidate points; and repeating the process until an optimal candidate point is converged upon.
H04N 21/8549 - Creating video summaries, e.g. movie trailer
G06F 16/783 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
13.
SYMMETRICAL LIGHT BAR LAYOUT FOR AUDIO/VISUAL DEVICE
The present disclosure describes devices, components, connectors, and cables that provide backlight for a display screen. Some embodiments describe a light-emitting diode (LED) bar or a plurality of LED bars. The LED bars can include a communications port on a first end of each LED bar that is configured to receive a driving voltage, data signals, and/or some combination of power and data from a control source. The LED bars can include a power port on a second end of each LED bar that is configured to receive power from a power source. The light bars can be arranged substantially parallel to one another and in a matching orientation.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for optimizing continuous parameters in online recommendation systems. An example embodiment operates by generating a surrogate model including an updatable statistical model of one or more mappings from candidate configurations of tunable parameters of a target model to values of the objective function. The tunable parameters include a continuous parameter. The embodiment then selects, using an acquisition function and outputs from the surrogate model, candidate configurations of the tunable parameters having the continuous parameter. The embodiment then determines objective-function values that are indicative of performance measures of the target model for the selected candidate. The embodiment then updates the surrogate model. The embodiment then selects a configuration of the tunable parameters that include the continuous parameter having a value associated with an extremum of the performance measures of the target model.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for enhancing images and/or videos captured by a camera. An example aspect operates by receiving, by at least one computer processor on the camera, a video frame including a first set of pixels. The method further includes performing the video processing on the first set of pixels to generate a second set of pixels before compressing or encoding the received video frame. The method further includes compressing the second set of pixels and causing the compressed second set of pixels to be displayed on a display device.
H04N 23/80 - Camera processing pipelinesComponents thereof
H04N 19/172 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
H04N 19/182 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a pixel
H04N 19/60 - Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using transform coding
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for automatic preview generation for videos. An example embodiment operates by identifying content for which to generate a preview and a plurality of shots within the content. A subset of frames is selected based on a sharpness indicator. An aesthetic score for each of the subset of sharpest frames is generated based both a positive score and a negative score for each frame as generated by a visual language model (VLM). A preview frame is selected, and a preview is generated for the content based on the preview frame. The preview of the content is provided for display.
System, apparatus, article of manufacture, method and/or computer program embodiments are provided for automated quality control (QC) of media content. An example method can include segmenting, via a first thread implemented by a first program at a node, a video into video segments including video and audio content; providing, via a second thread implemented by the first program, each video segment to a second program at the node; as the second thread provides each video segment to the second program, removing, from storage via a third thread implemented by the first program, one or more video segments provided to the second program by the second thread; for each video segment received by the second program, determining, via the second program, whether the video segment contains a QC issue including monophonic sound, silent/muted audio, black video frames, and/or frozen video frames; and generating QC data comprising information about QC issues detected.
H04N 21/24 - Monitoring of processes or resources, e.g. monitoring of server load, available bandwidth or upstream requests
H04N 21/231 - Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers or prioritizing data for deletion
H04N 21/233 - Processing of audio elementary streams
H04N 21/234 - Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for dynamic tone mapping of video content. An example embodiment operates by identifying, by a dynamic tone mapping system executing on a media device, characteristics of a first video signal having a first dynamic range based on a frame-by-frame analysis of the first video signal. The example embodiment further operates by modifying, by the dynamic tone mapping system, a tone mapping curve based on the characteristics of the first video signal to generate a modified tone mapping curve. Subsequently, the example embodiment operates by converting, by the dynamic tone mapping system, the first video signal based on the modified tone mapping curve to generate a second video signal having a second dynamic range that is less than the first dynamic range.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for altering the appearance of electronic devices. An example process can include obtaining a visibility configuration for altering an appearance of an exterior surface of an electronic device; and modifying at least a portion of the exterior surface of the electronic device based on the visibility configuration.
G08B 13/196 - Actuation by interference with heat, light, or radiation of shorter wavelengthActuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
H04N 7/18 - Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
H04N 23/61 - Control of cameras or camera modules based on recognised objects
H04N 23/695 - Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
System, apparatus, article of manufacture, method and/or computer program embodiments relate to providing a virtual assistant avatar. An example method can include identifying a user of a device, determining a profile for a virtual assistant avatar associated with the user, the profile for the virtual assistant avatar comprising at least one of a visual component, a voice component, and a behavioral component, selecting an operational mode for the virtual assistant avatar based on a current user interface (UI) context, generating the virtual assistant avatar for the user based on the operational mode and the profile for the virtual assistant avatar associated with the user, and providing the virtual assistant avatar to at least one of a user interface (UI) and the device associated with the user.
System, apparatus, article of manufacture, method and/or computer program embodiments are provided for customizing user interfaces (UIs) based on custom themes and animations. An example method can include determining topic candidates for a theme of a UI of an application at a device associated with a user; determining a respective selection parameter for each topic from the topic candidates, the respective selection parameter comprising a topic preference score, a topic priority, a topic metric, and/or a topic relevance to a date associated with a presentation of the UI, the user, the application, and/or content associated with the application; selecting a topic from the topic candidates based on the respective selection parameter for each topic from the topic candidates; based on the topic, determining a theme for the UI of the application; and generating UI data defining a customized configuration of the UI based on the theme.
G06F 3/04845 - Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range for image manipulation, e.g. dragging, rotation, expansion or change of colour
G06F 3/04842 - Selection of displayed objects or displayed text elements
24.
CUSTOMIZED USER INTERFACES AND MEDIA CONTENT EXPERIENCES
System, apparatus, article of manufacture, method and/or computer program embodiments are provided for customizing user interfaces (UIs) and UI experiences based on UI themes and features. An example method can include determining features of a UI of an application, the features comprising a content item, functionality, setting, and/or UI destination; selecting a feature from the features based on a theme of the UI and/or a respective selection parameter of each feature, the respective selection parameter defining a feature preference, priority, metric, and/or relevance with respect to a date associated with a presentation of the UI, a user, the application, and/or feature topic; generating, via an AI model, animated content for display at the UI, the animated content depicting the feature and/or an interaction between the animated content and a portion of the UI associated with the feature; and providing the animated content to the UI and/or a device.
System, apparatus, article of manufacture, method and/or computer program embodiments are provided for customizing user interfaces (UIs) and profiles based on themes and content. An example method can include identifying a user associated with a UI of an application; selecting visual attributes of the UI, the visual attributes being selected based on a theme of the UI and/or a respective selection parameter associated with each of a set of content items of the UI, the respective selection parameter defining a content preference, priority, metric, and/or a relevance to a date associated with a presentation of the UI, the user, the application, and/or a topic; generating, via an AI model, a profile image of the user customized based on the visual attributes; and providing the profile image of the user to the UI and/or a device associated with the user.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content recommendation system powering a streaming media publisher channel, in conjunction with an object recognition model, to enhance dynamic generation of a banner being shown to a user via an awareness or performance campaign. This method allows the platform to present the most relevant ML personalized in-channel content to the publisher platform users in endemic banners that run on the platform which then correspondingly helps drive user reach. An example embodiment operates by implementing personalized content banners that may act as a hook for channel users opening their streaming device, both active and lapsed, to enter back into the channel.
Disclosed herein are computing system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for distributing processing of sensor data. For example, a computing system may be configured to determine processing information of a plurality of edge devices associated with a first network. Additionally, the computing system may select, from the plurality of edge devices, a target device, based on the processing information of each of the plurality of edge devices. Moreover, the computing system may, based on one or more privacy preferences that define one or more types of information to be provided to one or more computing systems outside of the first network, provide, via the target device, one or more feature vectors of one or more objects in a corresponding environment to a second computing system associated with a second network.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing the temperature of a peripheral device to a computing device. A controller can receive an indication signal from the peripheral device and generate a control signal based on the received indication signal. The controller can be configured to generate the control signal based on a temperature within a cooling space comprising the peripheral device.
A method is described and includes receiving by a content link sharing system a request for a content link associated with content being viewed by a user; generating by the content link sharing system the requested content link; and providing by the content link sharing system the generated content link, wherein the content link is configured to be copied by the user and shared with at least one recipient.
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for implementing an Internet of Things (IoT) application store. An embodiment provides a digital marketplace service and a smart installation service. The digital marketplace service enables a user to browse a plurality of IoT applications and select one for installation. The smart installation service determines an input or an output of the selected IoT application, selectively assigns a first IoT device in a set of IoT devices associated with the user to the input or the output, and installs an instance of the selected IoT application on one or more devices that comprise or are communicatively connected to the first IoT device, wherein the installing comprises configuring the instance of the selected IoT application to obtain the input from the first IoT device or provide the output to the first IoT device.
Described herein are various embodiments for customized device pairing based on device features. An embodiment operates by receiving, from a first device, a message indicating motion sensing capability of the first device available for pairing the first device with a second device, wherein the first device does not include an alphanumeric keypad. A sequence of actions to be performed on the first device is generated. The sequence of actions is provided for display. An indicia indicating a set of one or more actions performed on the first device is received. It is determined that the set of one or more actions of the indicia corresponds to the sequence of actions provided for display, and the first device is paired with a second device.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for evaluating bitrate selection models. An example embodiment operates by aggregating data associated with a data streaming session comprising a plurality of segments. The embodiment then determines, for each segment of the plurality of segments, segment data comprising streaming parameters by analyzing the aggregated data. The embodiment then generates a simulation input based on the segment data for each segment. The embodiment then performs a first simulation, based at least on the segment data for each segment and a first bitrate selection model, to simulate streaming each segment. The embodiment then generates a first simulation result for each segment. The embodiment then aggregates each first simulation result to provide a first aggregate simulation result of the data streaming session.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for processing, understanding, and defining media content. An example process can include receiving, from a media device, a user input indicative of a preferred level of exposure to targeted media content; configuring, based on the user input, one or more playback settings associated with a media content item to accommodate a customized amount of the targeted media content during playback of the media content item; and sending, to the media device, the media content item and the customized amount of the targeted media content.
H04N 21/458 - Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming streamUpdating operations, e.g. for OS modules
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies or resolving scheduling conflicts
H04N 21/466 - Learning process for intelligent management, e.g. learning user preferences for recommending movies
H04N 21/475 - End-user interface for inputting end-user data, e.g. PIN [Personal Identification Number] or preference data
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for establishing a streaming channel between a wireless communication device and a streaming device through a proximity device. The streaming device establishes a first connection with a proximity device. Afterwards, the streaming device can retrieve a list of available wireless channels to communicate with the wireless communication device, where the list of available wireless channels is selected from a list of wireless channels provided by the wireless communication device. The streaming device can further establish a second connection by the streaming device with the wireless communication device through a streaming channel selected from the list of available wireless channels.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for mitigating performance degradation of a computing device. In an embodiment, a notification indicative of a characteristic of a compute resource of the device is received. The notification is of a first type of a plurality of types of notifications, and each type of notification of the types of notifications is indicative of a different characteristic of the compute resource. A level of performance degradation, from levels of performance degradation, of the computing device is determined based on a mapping that maps each type of notification of the types of notifications to a corresponding level of performance degradation of the different levels of performance degradation. An action from a plurality of different actions configured to mitigate the performance degradation is performed based on the determined level of performance degradation.
Automatic Classification of Audio Content as Either Primarily Speech or Primarily Non-Speech, to Facilitate Dynamic Application of Dialogue Enhancement
A method for dynamically controlling enhancement of an audio stream is provided, where the audio stream defines a sequence of audio segments over time. Each audio segment defines a waveform having a plurality of waveform attributes. For each audio segment of the sequence of audio segments, the method includes: (i) determining a set of waveform-attribute values of the audio segment's waveform attributes, (ii) computing a first distance between the determined set of waveform-attribute values and a first predefined set of waveform-attribute values representative of speech, and computing a second distance between the determined set of waveform-attribute values and a second predefined set of waveform-attribute values representative of music, (iii) using the computed first and second distances as a basis to classify the audio segment as primarily speech or rather primarily music, and (iv) controlling, based on the classifying, whether or not to enhance the audio segment for output.
G10L 25/18 - Speech or voice analysis techniques not restricted to a single one of groups characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
38.
STOCHASTIC RANK BOOST FACTORS TO OPTIMIZE ENGAGEMENT AND REVENUE
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for stochastic multi-period multi-objective optimization based recommendation system. An embodiment assigns a respective plurality of stochastic parameters to a plurality of recommendation objectives. The embodiment further associates each program of a plurality of programs with one or more recommendation objectives of the plurality of recommendation objectives, and selects, during a first recommendation time period, a first set of operative recommendation objectives from the plurality of recommendation objectives based on the plurality of stochastic parameters. The embodiment then generates a first ordered list of recommended programs from the plurality of programs based on the first set of operative recommendation objectives. The embodiment dynamically reorganizes, during the first recommendation time period, programs displayed on a graphical user interface (GUI) based on the first ordered list of recommended programs.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for mitigating performance degradation of a computing device. In an embodiment, configuration setting(s) for mitigating performance degradation of the computing device are received from a data source external to the computing device. An order of actions for mitigating the performance degradation is determined based on the configuration setting(s). A notification that utilization of at least one compute resource of the computing device has met a degradation condition is received. Action(s) to mitigate the performance degradation are performed based on the notification in accordance with the determined order of actions.
System, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, generate a prediction based on a query. A query is provided to a deep machine learning (ML) model. The deep ML model generates a plurality of query projection embeddings by projecting the query into each of a plurality of different query embedding spaces and generates the prediction based at least on the plurality of query projection embeddings. Each of a plurality of query projection embedding layers of the deep ML may generate a corresponding one of the query projection embeddings by applying a hash function associated with the query projection layer to the query to generate a vector representation of the query, and applying a set of weights associated with the query projection layer to the vector representation to generate a query projection embedding in the plurality of query projection embeddings.
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for optimizing content item placements on a content publisher application of a media device based on multi-publisher content item measurement data associated with a set of media devices. An example embodiment operates by receiving multi-publisher content item measurement data that comprises data signals for a plurality of content items from a plurality of different publishers that are presented at a plurality of different households. A content item, from among the plurality of content items, is determined to be transmitted to a media device of a particular household for presentation via a content publisher application of the media device. In response to determining that the content item is to be transmitted to the media device, the content item is transmitted to the media device for presentation via the content publisher application of the media device.
Aspects of the disclosed technology provide solutions for searching objects within multimedia content based on multi-modal embeddings. An example method can include receiving media content including a plurality of video frames. The method can include steps for generating, using a pre-output layer of a machine learning algorithm, one or more multimodal feature embeddings describing at least one object for the plurality of video frames, receiving a query including a request to search the media content for a matching object, determining whether the media content includes the matching object based on the one or more multimodal feature embeddings describing the at least one object, and returning one or more results in response to determining that the media content includes the matching object. Systems and machine-readable media are also provided.
Embodiments present techniques for determining a list of recommended items in response to a user query. An embodiment can determine a first ordered list of items including a plurality of items stored by a content platform. Based on a reward discount parameter, a first total discounted future reward for the first ordered list of items can be determined. Based on a risk discount parameter, a first risk estimate for the first ordered list of items can be determined. Similarly, a second ordered list of items can have a second total discounted future reward and a second risk estimate. The second ordered list of items can be the list of recommended items when the second total discounted future reward is larger than or equal to the first total discounted future reward, and the second risk estimate is less than or equal to the first risk estimate.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for controlling a backlight apparatus in a media device with driver ICs. In some embodiments, the media device can include a display screen and the backlight apparatus. In some embodiments, the backlight apparatus can include first and second LEDs in a first horizontal zone and third and fourth LEDs in a second horizontal zone. The backlight apparatus can include a first driver IC connected to the first and third LEDs and configured to turn on the first LED during a first time period and the third LED during a second time period. The backlight apparatus can include a second driver IC connected to the second and fourth LEDs and configured to turn on the second LED during the first time period and the fourth LED during the second time period.
G09G 3/34 - Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix by control of light from an independent source
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for user-based content filtering. A type of content indicated by a user profile associated with a request for a content item may be identified. An occurrence of the type of content within the content item may be identified. Filtering data that comprises one or more instructions for a media device to modify (e.g., blur, alter, obfuscate, etc.) a visual representation of the identified occurrence of the type of content during playback may be generated. The content item and the filtering data may be sent to a media device associated with the user profile.
H04N 21/454 - Content filtering, e.g. blocking advertisements
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies or resolving scheduling conflicts
46.
REDUCING ACTIVE USER BIAS FOR CONTENT RECOMMENDATION MODELS
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
47.
UTILIZING A SINGLE BUFFER FOR A DYNAMIC NUMBER OF PLAYERS, EACH USING A DYNAMICALLY SIZED BUFFER
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a single storage buffer for a dynamic number of players, each using a dynamically sized virtual buffer. For example, a system includes a buffer management controller that receives a request to initiate at least one player instance for displaying a content item. The buffer management controller creates a virtual buffer having a maximum capacity for the player instance. Finally, the buffer management controller identifies one or more available regions of the single storage buffer and maps the virtual buffer to the one or more available regions in response to determining that address space in the one or more available regions matches or exceeds the maximum capacity of the virtual buffer.
H04N 21/231 - Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers or prioritizing data for deletion
48.
PERSONALIZED MULTIMODAL ANALYSIS FOR CONTENT ITEM RECOMMENDATION
Disclosed herein are system, apparatus, article-of-manufacture, method and/or computer program product embodiments, and/or combinations/sub-combinations thereof, for personalized multimodal analysis for content item recommendation. An embodiment operates by identifying playback of a first content item by a user device, and simulating playback of a second content item with a modality feature that matches the first content items. Affinity for a modality of the second content item is identified based on weights assigned to the different modalities according to the simulated playback. Respective similarity scores are generated for a plurality of content items based on a similarity between a vector for an embedding indicative of the modality for the second content item and a respective vector for an embedding indicative of the modality generated for the content items. Indication of a set of content items with respective similarity scores that satisfy a similarity score threshold is sent to the user device.
Surround sound systems can dramatically expand the size of a user's sound field. Much surround sound content is mixed in a simplistic way where the front audio is copied to the rear, at a lower volume. It can be difficult for users to appreciate the value proposition of a surround sound system without more compelling spatially complex content. Quantifying surround sound complexity of various content items based on an audio spatial complexity scoring system can address this issue. Algorithms can be implemented to determine an audio spatial complexity score based on audio channels of a content item. Large catalog of content items can be analyzed, and audio spatial complexity scores can be associated with various content items. If a user has a surround sound system, content items with a high audio spatial complexity can be retrieved or recommended to the user to demonstrate the surround sound system's value better.
In one aspect, an example method includes: (i) receiving media content comprising video content and audio content; (ii) providing, to a trained machine-learning model, video data associated with the video content; (iii) responsive to the providing, receiving, from the trained machine-learning model, supplemental audio content that was generated by the trained machine-learning model based at least on the provided video data; and (iv) modifying the received media content at least by adding the generated supplemental audio content to the media content.
H04N 21/233 - Processing of audio elementary streams
H04N 21/25 - Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication or learning user preferences for recommending movies
Disclosed herein are system, method, and computer program product embodiments that use artificial intelligence (AI) to generate content recommendation micro-descriptors for use in a content selection graphical user interface (GUI). An embodiment operates by determining whether a content release date derived from stored metadata corresponding to media content is before a training cutoff date of a generative AI model. If so, a first prompt, including content title, content type, and content release year, instructs a generative AI model to generate a first description for the content, which is provided in a second, summarization prompt to the generative AI model to generate a second description for the content. The second description is concise, e.g., five tokens or fewer, and is displayed in the content selection GUI.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content recommendation system powering a streaming media publisher channel to enhance an ad creative being shown to the user via awareness or performance campaigns. This method allows the platform to present exploratory personalized in-channel content to the publisher platform users in endemic banners that run on the platform which then correspondingly helps drive user reach. An example embodiment operates by implementing personalized content banners that may act as a hook for channel users opening their streaming device, both active and lapsed, to enter back into the channel.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content recommendation system powering a streaming media publisher channel to enhance an ad creative being shown to the user via awareness or performance campaigns. This method allows the platform to present the most relevant Machine Language (ML) personalized in-channel content to the publisher platform users in endemic banners that run on the platform which then correspondingly helps drive user reach. An example embodiment operates by implementing personalized content banners that may act as a hook for channel users opening their streaming device, both active and lapsed, to enter back into the channel.
H04N 21/431 - Generation of visual interfacesContent or additional data rendering
H04N 21/458 - Scheduling content for creating a personalised stream, e.g. by combining a locally stored advertisement with an incoming streamUpdating operations, e.g. for OS modules
54.
CONTEXT CLASSIFICATION OF STREAMING CONTENT USING MACHINE LEARNING
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for performing context classification of streaming content using machine learning (ML). In an embodiment, a streaming media client receives an audio/video (A/V) stream that represents a portion of content to be played back by the client. The client reconstructs a sequence of video frames from the A/V stream, extracts audio information from the A/V stream, and executes an ML based classifier to predict a context label associated with the portion of content based at least on one or more video frames from the sequence of video frames and the audio information. The client then transmits the context label to a streaming media service. The service may use the context label to select an advertisement or content recommendation to send to the client or to select a set of content streaming parameters for the client.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for dynamically adjusting sound output of a multi-speaker configuration of a display device. The adjustment may be performed to optimize the sound output based on the physical position of the display device in relation to one or more microphones within a physical environment. An example embodiment operates by the speakers within the display device emitting calibration sound waves and receiving sound data associated with the calibration sound waves. The sound data includes characteristics of the calibration sound waves within the physical environment. The display device may then adjust sound output of speakers within the multi-speaker configuration based on the sound output characteristics.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for adjusting a display setting of a display device by receiving a display status value from a sensor and determining that a display value is outside a predetermined range. An example embodiment operates by receiving the display status value of the display device from the sensor, where the display status value is determined based on at least one of a brightness of the display device and a color value of the display device. The example embodiment further operates by determining that the display status value is outside a predetermined range. The example embodiment further operates by adjusting at least one of the brightness of the display device and the color value of the display device in response to the determination, thereby controlling the display setting of the display device.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for dynamically adjusting sound output of a multi-speaker configuration of a display device. The adjustment may be performed to optimize the sound output based on the physical position of the display device in relation to one or more microphones within a physical environment. An example embodiment operates by the speakers within the display device emitting calibration sound waves and receiving sound data associated with the calibration sound waves. The sound data includes characteristics of the calibration sound waves within the physical environment. The display device may then adjust sound output of speakers within the multi-speaker configuration based on the sound output characteristics.
H04S 7/00 - Indicating arrangementsControl arrangements, e.g. balance control
H04S 5/00 - Pseudo-stereo systems, e.g. in which additional channel signals are derived from monophonic signals by means of phase shifting, time delay or reverberation
Streaming of video and audio material on the Internet; Streaming of video material on the Internet; Streaming of audio material on the Internet; Electronic transmission and streaming of digital media content for others via global and local computer networks
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for recommending content items. For example, a first content item unassociated with interaction-based data is determined. A description-based representation of the first content item, an image-based representation of the first content item, and/or a metadata-based representation of the first content item is obtained from machine learning model(s). Such representation(s) are provided as an input to a neural network. A first interaction-based representation of the first content item based on such representation(s) is received as an output from the neural network. A measure of similarity is determined between the first interaction-based representation and second interaction-based representation(s) of second content item(s). A determination is made, based on the measure of similarity, that the first content item is to be recommended, and an indication recommending the first content item is outputted.
Aspects of the disclosed technology provide solutions for processing media content to generate customized media content of a target duration. An example computer-implemented method can include classifying each of a plurality of video frames of a media content having a first duration as a critical frame or an uncritical frame; generating a target media content including a set of video frames of the plurality of video frames that are each classified as the critical frame, the set of video frames having a runtime duration that meets a predetermined runtime duration; and presenting the target media content via a display device. Systems and machine-readable media are also provided.
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for adjusting a display setting of a display device by receiving a display status value from a sensor and determining that a display value is outside a predetermined range. An example embodiment operates by receiving the display status value of the display device from the sensor, where the display status value is determined based on at least one of a brightness of the display device and a color value of the display device. The example embodiment further operates by determining that the display status value is outside a predetermined range. The example embodiment further operates by adjusting at least one of the brightness of the display device and the color value of the display device in response to the determination, thereby controlling the display setting of the display device.
G09G 3/20 - Control arrangements or circuits, of interest only in connection with visual indicators other than cathode-ray tubes for presentation of an assembly of a number of characters, e.g. a page, by composing the assembly by combination of individual elements arranged in a matrix
63.
Real-time online learning for short-form content ranking
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for real-time online learning for short-form content ranking. An example embodiment operates by receiving a user feedback event that includes user feedback on a short-form content item played using a media device, and publishing a message with the event to a publish-subscribe (pub/sub) topic. Retrieved with a pull operation, the user feedback event from the message is saved to a real-time data store. A recommendation engine is triggered to generate a playlist batch of short-form content recommendations based on the user feedback event. Short-form content items specified in the playlist batch are transmitted to the media device for playback. User feedback on short-form content items in an immediately preceding playlist batch during the same usage session can inform the selections made in the generated playlist batch.
H04N 21/262 - Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission or generating play-lists
H04N 21/258 - Client or end-user data management, e.g. managing client capabilities, user preferences or demographics or processing of multiple end-users preferences to derive collaborative data
H04N 21/2668 - Creating a channel for a dedicated end-user group, e.g. by inserting targeted commercials into a video stream based on end-user profiles
64.
Media System with Presentation Area Data Analysis and Segment Insertion Feature
In one aspect, disclosed is an example method for use in connection with a media-presentation device having an associated presentation area, the method including: (i) obtaining presentation area data associated with the presentation area; (ii) determining that the obtained presentation area data satisfies each and every condition of a condition set; (iii) responsive to at least determining that the obtained presentation area data satisfies each and every condition of the condition set, identifying an upcoming media segment insertion point within a media program; and (iv) facilitating the media-presentation device outputting for presentation a media segment starting at the identified media segment insertion point within the media program.
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
H04N 21/442 - Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed or the storage space available from the internal hard disk
In one aspect, a method includes receiving video content comprising a first data portion comprising parameters that control a visual appearance of frames of video content presented by a content-presentation device. The content-presentation device is communicatively coupled to a light unit that is (i) present in a viewing area of the content-presentation device and (ii) configured to provide ambient light in the viewing area. The method also includes extracting, from the received video content, a second data portion, separate from the first data portion, specifying one or more light control parameters that a controller associated with the light unit is configured to interpret as instructions for controlling the light unit. The method also includes transmitting the extracted second data portion to the controller to facilitate the controller controlling the light unit according to the specified one or more light control parameters.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for cue point discovery for content. For example, system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof are provided for using unsupervised machine learning to automatically classify cue points for episodic content. The cue points can be associates with an opening credits section, an end credits section, a recap section, or a behind-the-scenes section.
G06V 20/40 - ScenesScene-specific elements in video content
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
G06V 20/62 - Text, e.g. of license plates, overlay texts or captions on TV images
67.
CONTEXTUAL UNDERSTANDING OF MEDIA CONTENT TO GENERATE TARGETED MEDIA CONTENT
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for processing, understanding, and defining video content. An example can include determining a plurality of contextual features associated with at least one part of a media content item; identifying one or more targeted media content items that are associated with at least one contextual feature from the plurality of contextual features; selecting, based on the at least one contextual feature, a targeted media content item from the one or more targeted media content items, wherein the targeted media content item includes content that is contextually related to the at least one part of the media content item; and presenting the targeted media content item in association with playback of the at least one part of the media content item.
G06V 10/70 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies or resolving scheduling conflicts
68.
Speaker-Identification Model for Controlling Operation of a Media Player
In one aspect, an example method includes (i) obtaining, by a media player of a media presentation system, an audio signal, where the audio signal includes a voice command and is obtained using a microphone of the media presentation system; (ii) identifying, by the media player, which of multiple speakers of a household uttered the voice command using the audio signal and a speaker-identification model; (iii) performing, by the media player, an action corresponding to the voice command; and (iv) based on the identifying of the speaker using the audio signal and the speaker-identification model, selecting, by the media player, a user profile associated with the identified speaker within a streaming channel so as to bypass a profile selection screen of the streaming channel.
H04N 21/45 - Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies or resolving scheduling conflicts
H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
H04N 21/4415 - Acquiring end-user identification using biometric characteristics of the user, e.g. by voice recognition or fingerprint scanning
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
69.
OBJECT INJECTION FRAMEWORK FOR DYNAMIC AND INTERACTIVE SCREENSAVER
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for dynamically adjusting a screensaver on a display device. An example embodiment operates by a media device detecting the initiation of a screensaver and requesting replacement objects to be injected into the screensaver while the screensaver is displayed on a display device. The media device may receive the replacement objects, select which replacement objects to be injected into the screensaver, and injecting the selected replacement objects into an off-screen portion of the screensaver prior to the off-screen portion being displayed on the displayed device.
Embodiments described herein generally relate to analyzing a signal generated by a device under test (DUT). In particular, the signal generated by the DUT may be compared to a reference signal to determine pass/fail results for the DUT. For example, a method may include: storing, on a computing device, a reference signal from a reference device; receiving a test signal from a device under test (DUT); synchronizing the reference signal and the test signal based on a time-synchronization buffer of each signal; after the synchronization, comparing the test signal and the reference signal to determine a pass or fail result for the DUT; and generating a notification indicating the pass or fail result for the DUT.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for an application pre-launch system. An example embodiment operates by detecting a user interaction associated with launching an app configured to operate on a computing device. A first launch command is provided to a server associated with the app. The first launch command is configured to instruct the server to begin loading application data for the app into a cache. Application code of the app is executed, wherein the application code is configured to provide a second launch command to the server. The application data is received from the cache responsive to the second launch command. The app is launched on the computing device using the application data received from the cache. After the launching, the app is configured to receive a user command.
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof for avoiding slate playback during live streams through dynamic content filling. An example embodiment operates by receiving, from an origin server, a manifest associated with live streaming content, analyzing the manifest to identify an advertising time window in the live streaming content, wherein the manifest specifies a slate to be played during the advertising time window, determining a portion of the advertising time window that cannot be filled by advertisements identified by an advertising decision server, identifying one or more content items to be played back during the portion of the advertising time window, updating the manifest to specify that the one or more content items are to be played back during the portion of the advertising time window instead of the slate, and transmitting the updated manifest to a media player.
H04N 21/262 - Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission or generating play-lists
System, apparatus, article of manufacture, method and/or computer program embodiments are provided for delivering a secondary content item based on a user return event. An example method may include detecting a user return event; pre-fetching and pre-buffering a secondary content item during an idle period preceding the user return event; and initiating a transition effect based on the user return event, the transition effect includes partially displacing a home screen interface and displaying the secondary content item in a defined region of a screen.
H04N 21/443 - OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB
H04N 21/231 - Content storage operation, e.g. caching movies for short term storage, replicating data over plural servers or prioritizing data for deletion
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
Disclosed herein are system, apparatus, device, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for using large language models and text embedding models to generate trivia questions. An example aspect operates by a computer-implemented method. The method includes receiving, by at least one computer processor, a category for a plurality of trivia questions and receiving a difficulty level for the plurality of trivia questions. The method further includes using the category to retrieve additional information for the plurality of trivia questions and generating the plurality of trivia questions based at least on the additional information and the difficulty level. The method further includes displaying the plurality of trivia questions on a display device.
G06F 40/40 - Processing or translation of natural language
A63F 13/60 - Generating or modifying game content before or while executing the game program, e.g. authoring tools specially adapted for game development or game-integrated level editor
Provided are system, apparatus, device, method and/or computer-program product embodiments, combinations and/or sub-combinations thereof for using an AI model to facilitate natural language interactions with databases. An example method can include receiving a natural language prompt associated with a user and identifying tables in a database based on the natural language prompt. The method can further include determining a table schema(s) of each of the tables identified, generating, using a large language model, a query to the tables in the database based on the natural language prompt and the table schema(s), and obtaining, using the query, data from at least one table of the tables in the database. The method can include generating, using the large language model or another large language model, a response to the natural language prompt based on the data obtained from the at least one table of the tables in the database.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for automatic analysis and dynamic selection or creation of high quality supplemental content for a program (e.g., movies and TV shows) to maximize user engagement and the consumption of the media stream content by users. An example embodiment operates by using different machine learning (ML) models and large language models (LLMs) on existing supplemental content for the program to extract potential engaging features. The embodiment then conducts multivariate testing on the extracted potential engaging features to identify engaging features that improve user engagement for a user or a group of users. Based on the identified engaging features, the embodiment then selects high quality supplemental content from the video stream or alternatively creates high quality supplemental content using artificial intelligence (AI) when no high quality supplemental content exists for the video stream.
Aspects of the disclosed technology provide solutions for customizing audio streams for individual users. An example process can include steps for receiving an audio stream, establishing a connection with a first audio device associated with a first user, establishing a connection with a second audio device associated with a second user, and delivering a first segment of the audio stream to the first audio device based on user preferences associated with the first user. The process can further include steps for delivering a second segment of the audio stream to the second audio device based on user preferences associated with the second user. Systems and machine-readable media are also provided.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for determining an optimal supplemental content for a media stream menu interface to maximize the consumption of the media stream content by users. An example embodiment operates by performing automated content recognition (ACR) on the media stream, thereby determining optimal supplemental content. The embodiment identifies a plurality of potential supplemental content items in the media stream based on the characteristics of the media stream. The embodiment then outputs the optimal supplemental content to a plurality of predetermined media devices.
H04N 21/2668 - Creating a channel for a dedicated end-user group, e.g. by inserting targeted commercials into a video stream based on end-user profiles
System, apparatus, article of manufacture, method and/or computer program embodiments are provided for implementing smart video experiences. An example method includes receiving a request to perform a video control operation during playback of a video; based on a type of video control operation of the video control operation and a playback position of the video, selecting one or more video markers for the video control operation from a plurality of video markers associated with respective video frames from a plurality of video frames of the video, the one or more video markers being associated with one or more video frames of the video; selecting the one or more video frames for the video control operation based on the one or more video markers; and generating a signal configured to trigger the video control operation based on the one or more video frames associated with the one or more video markers.
H04N 21/472 - End-user interface for requesting content, additional data or servicesEnd-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification or for manipulating displayed content
G06V 10/74 - Image or video pattern matchingProximity measures in feature spaces
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 20/40 - ScenesScene-specific elements in video content
H04N 21/845 - Structuring of content, e.g. decomposing content into time segments
80.
Computing System with Multi-Layered and Unified Machine-Learning Model
In one aspect, an example method is provided. The method includes receiving media content; providing the received media content to a first portion of layers of a trained machine-learning model, wherein the first portion of layers of the trained machine-learning model is configured to run within a trusted execution environment of a computing system; receiving, from the first portion of layers of the trained machine-learning model, extracted feature data of the provided media content; providing the extracted feature data to a second portion of layers of the machine-learning model, wherein the second portion of layers of the machine-learning model is configured to run outside the trusted execution environment of the computing system; receiving, from the second portion of layers of the machine-learning model, output data; and using the received output data to perform an action.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06V 10/77 - Processing image or video features in feature spacesArrangements for image or video recognition or understanding using pattern recognition or machine learning using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]Blind source separation
G06V 10/94 - Hardware or software architectures specially adapted for image or video understanding
G06V 30/18 - Extraction of features or characteristics of the image
G06V 40/10 - Human or animal bodies, e.g. vehicle occupants or pedestriansBody parts, e.g. hands
81.
LIVE PROGRAM DEDUPLICATION USING VIDEO FINGERPRINTING
Disclosed herein are system, method and/or computer program product embodiments, and/or combinations thereof, for deduplication of live programming. An embodiment generates a plurality of video on demand (VOD) hash character strings corresponding to a plurality of VOD programs. The embodiment also generates a plurality of live-video hash character strings corresponding to a live video program. The embodiment then determines a match measure between the plurality of live-video hash character strings and a plurality of VOD hash character strings corresponding to the plurality of VOD programs. The embodiment performs deduplicating of the live video program based on a determination that the match measure exceeds a threshold value. The embodiment then transmits the live video program by assigning metadata corresponding to the VOD program of the plurality of VOD programs to the live video program.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for transferring streaming media playback between streaming media devices. In an example embodiment, a user may be streaming content using a first media device. The user may then send a command to the first media device to transfer the streaming content to a second media device. The first media device may identify the second media device as being on a common network and transmit a wake command to the second media device as well as media playback state information indicating a playback position to resume playback. In some embodiments, the first media device may download the media playback state information onto a mobile device. The mobile device may then provide the media playback state information to the second media device to resume playback at the first media device's playback position.
H04N 21/43 - Processing of content or additional data, e.g. demultiplexing additional data from a digital video streamElementary client operations, e.g. monitoring of home network or synchronizing decoder's clockClient middleware
G06F 1/3206 - Monitoring of events, devices or parameters that trigger a change in power modality
H04N 21/41 - Structure of clientStructure of client peripherals
H04N 21/422 - Input-only peripherals, e.g. global positioning system [GPS]
H04N 21/443 - OS processes, e.g. booting an STB, implementing a Java virtual machine in an STB or power management in an STB
H04N 21/6587 - Control parameters, e.g. trick play commands or viewpoint selection
A computer-implemented method is described and includes monitoring an audio signal received at a television to detect a sustained increase in a perceived loudness of the audio signal; subsequent to the detecting, providing a notification to a user regarding availability of a television volume leveling function; and subsequent to receiving from the user a response to the notification, wherein the response indicates an approval to activate the television volume leveling function, activating the volume leveling function. In particular embodiments, the monitoring is performed using Loudness Units relative to Full Scale (LUFS).
Provided are system, apparatus, device, method and/or computer-program product embodiments, combinations and/or sub-combinations thereof for determining, using an artificial intelligence (AI) model, content item placements based on text information associated with scripted content. An example method can include receiving textual information descriptive of one or more scenes associated with media content and, based on the textual information, determining, by an AI model, one or more contextual attributes of each scene from the one or more scenes. The method can include, based on the contextual attributes, determining one or more content placement locations within the one or more scenes, the one or more content placement locations depicting at least one of an object, an activity, and a dialogue. The method can further include generating one or more suggested content modifications for the at least one of the object, the activity, and the dialogue in the one or more content placement locations.
A method and system for evaluation of object motion repetitiveness as a basis to distinguish between human motion and non-human motion, in order to facilitate control of device operation. An example method includes (i) a computing system receiving sensor data representing object motion detected by at least one sensor, the object motion defining motion of an object, (ii) the computing system making a determination, based at least on an evaluation of the received sensor data, of whether the detected object motion is repetitive, and (iii) based at least on the determination being that the detected object motion is not repetitive, the computing system responding to the detected object motion by causing a device to take an action corresponding with a prediction that the object is a human being.
System, apparatus, article of manufacture, method and/or computer program embodiments are provided for determining a presentation configuration of content. An example method can include determining information about primary content displayed on a first display and an environment of the first display, the information including a display configuration of the primary content, a characteristic of the primary content, and/or an indication whether a user is present in the environment and/or the environment includes a second display coupled to a client or a different client that is coupled to the client; based on the information, determining a different display configuration for displaying the secondary content; and based on the different display configuration, generating an instruction to display the secondary content at the second display or on an ROI at the first display, the ROI excluding the primary content or including a portion of the primary content having a saliency below a threshold.
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating short-form content. An example aspect operates by analyzing a media file in a library using a machine learning model. To analyze the media file, the embodiment determines, using the machine learning model, a first portion of the media file that has a feature that satisfies a classification that the machine learning model is configured to identify. The embodiment tags the first portion using one or more position tags indicative of a beginning of the first portion of the media file or an end of the first portion of the media file. The embodiment then generates a segment from the media file based on the one or more position tags. The segment comprises the portion of the media file and excludes one or more second portions of the media file.
Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for code migration and deployment in an Internet-of-Things (IoT) environment. A system server (e.g., a server of a cloud-based platform, etc.) may receive a codebase and operational information for a target device (e.g., an IoT device, a mobile device, a smart device, etc.). The codebase may be modified for compatibility with the target device based on functionality for libraries of the codebase mapped to functional elements that define the functional capabilities of the target device. A codebase migration window may be identified based on an indication that an operation of the target device satisfies an operational threshold and the operational information received from the target device. The modified codebase may be transferred to the target device during the codebase migration window.
In one aspect, disclosed is an example method for use by a lighting device including a microphone and a light source, the method including: (i) receiving, by the lighting device via the microphone, an audio signal; (ii) decoding and extracting, by the lighting device, a lighting device control instruction from the received audio signal; and (iii) using, by the lighting device, at least the decoded and extracted lighting device control instruction as a basis to control operation of the light source.
Disclosed herein are system, apparatus, device, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for classifying audio signals and dynamically adjusting an audio processing based at least on the classification to create high quality audio. An example aspect operates by a computer-implemented method including receiving, by at least one computer processor, an audio signal associated with a content. The method further includes preprocessing the audio signal to generate preprocessed audio data and determining an audio class using the preprocessed audio data. The audio class indicates an audio mode for playing the audio signal. The method further includes outputting the audio signal and the audio class.
A cost effective big data retention management system can be implemented. The system may include a retention tagging system, which allows metastore tables managed by a query engine to be tagged with a retention policy. The system may include a partition semantics system, which can understand the retention tags and produce retention events. The system may include an event consumption system, which can process the retention events and execute a set of operations for the retention events. The system may include a data restoration system, which allows archived data to be restored as needed. The system may include a listener, which can enforce one or more rules relating to retention-related table properties.
G06F 16/11 - File system administration, e.g. details of archiving or snapshots
G06F 16/14 - Details of searching files based on file metadata
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for detecting downstream devices connected to an electrical load controlling device. An example embodiment operates by detecting an association signal from a downstream smart device responsive to a downstream smart device detection signal. The example embodiment further operates by determining whether the downstream smart device is coupled to an electrical terminal of an electrical switching device and configured to receive electricity in response to an actuation of the electrical switching device. If so, the example embodiment further operates by generating a control signal configured to instruct the electrical switching device to prevent a deactuation of the electrical switching device and transmitting the control signal to the electrical switching device.
H02J 13/00 - Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the networkCircuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
H01H 9/54 - Circuit arrangements not adapted to a particular application of the switching device and for which no provision exists elsewhere
Aspects of the disclosed technology provide solutions for controlling multiple displays (e.g., multiple display devices, multiple displays on a media device, etc.) using a remote control device. An example method can include assigning a code for each of a plurality of display devices based on an image of the plurality of display devices. In some instances, the code of each of the plurality of display devices enables a remote control to independently operate each of the plurality of display devices. Moreover, the example method can include determining a position of the remote control relative to each of the plurality of display devices based on the image. Further, the example method can include determining a display device of the plurality of display devices for the remote control to control based on the position of the remote control relative to each of the plurality of display devices.
On a television or media device, it is painfully slow for users to click through a settings tree to perform device tasks. To address this issue, a voice-based application can be implemented to help users perform and access device tasks by voice. A user can make an utterance to reach a specific page in the setting tree where the user can then complete the device task. It is not trivial to implement the application. It can be a challenge to determine the precise device task intent from the utterance when there are hundreds of device tasks. The type of device task intent and the context of the user device may impact the way the user interface is to be updated. Some device tasks may be unsupported by the user device. Voice hints to help users learn to use their voice can follow a unique logic for suppressing voice hints.
A method and a system for using crowdsourcing as a basis to predict and respond to emergency impact. An example method includes (i) a computing system receiving emergency-state reporting provided by multiple customer premises in a region, (ii) the computing system determining, based on the received emergency-state reporting provided by the multiple customer premises in the region, that a region-wide emergency situation exists in the region, and (iii) the computing system taking action, in response to the determining, based on the emergency-state reporting provided by the multiple customer premises in the region, that the region-wide emergency situation exists in the region.
On a television or media device, it is painfully slow for users to click through a settings tree to perform device tasks. To address this issue, a voice-based application can be implemented to help users perform and access device tasks by voice. A user can make an utterance to reach a specific page in the setting tree where the user can then complete the device task. It is not trivial to implement the application. It can be a challenge to determine the precise device task intent from the utterance when there are hundreds of device tasks. The type of device task intent and the context of the user device may impact the way the user interface is to be updated. Some device tasks may be unsupported by the user device. Voice hints to help users learn to use their voice can follow a unique logic for suppressing voice hints.
An apparatus is described and includes a portable media streaming device including an assembly that includes a USB-C port; and a switch integrated with the USB-C port, the switch generating a signal when the switch is actuated, the signal for triggering a factory reset of the portable media streaming device.
