Patent Application
20240406088
A system may employ an anomaly detection process to detect anomalies in data. It may be important to detect anomalies in real time such that any possible remedial actions can be performed as soon as possible. In some examples, a content delivery system may receive quality of service (QoS) data from different entities that are delivering content, such as content delivery networks and/or Internet Service Providers. However, it may be difficult to analyze the data for anomalies. For example, training a model to analyze the data may not address data drift, seasonality challenges, or be fast enough. For example, data may drift over time, which may require a new complete set of data in bulk to re-train the model. Also, seasonality may occur where different patterns may be experienced, which requires the model to be retrained again with a new batch of training data. The above training may slow down the process of detecting anomalies and cause a lag in the detection based on the retraining process.
The included drawings are for illustrative purposes and serve only to provide examples of possible structures and operations for the disclosed inventive systems, apparatus, methods and computer program products. These drawings in no way limit any changes in form and detail that may be made by one skilled in the art without departing from the spirit and scope of the disclosed implementations.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Described herein are techniques for a data analysis system. In the following description, for purposes of explanation, numerous examples and specific details are set forth to provide a thorough understanding of some embodiments. Some embodiments as defined by the claims may include some or all the features in these examples alone or in combination with other features described below, and may further include modifications and equivalents of the features and concepts described herein.
A system performs anomaly detection of data. In some examples, the system may operate a content delivery system that delivers content to client devices. The data that is monitored for anomalies may be based on the delivery of the content, such as quality of service (QOS) data that includes data that measures the quality of service while delivering content to client devices. However, the anomaly detection process may be used to prevent and detect anomalies for any system issues that may occur in different fields, such as network security, platform health monitoring, industrial Internet of things (IoT), user experience and management, etc.
The anomaly detection may detect anomalies in the data quickly, which allows a root cause of an issue to be identified and the issue to be addressed promptly. This may ensure a superior viewing experience at client devices as issues are resolved. In some examples, the system may operate in hundreds of geographical regions over numerous delivery entities, such as content delivery networks (CDNs), and Internet Service Providers (ISPs), that stream thousands of different content instances. The anomaly detection process needs to be scalable to monitor and track different QoS metrics that can detect anomalies in real-time at a large number of entities.
The system uses a machine learning-based, automatic, traffic loading agnostic, and start-type agnostic anomaly detection process that can monitor multiple variables that may be correlated in real time. The process may detect anomalies in correlations of multiple variables. For example, the two variables may be time and number of viewers. When the number of viewers at a time is abnormal, the anomaly may be detected. For example, 10 million viewers at 1:00 am may be an anomaly, but 1 million viewers at 1:00 am may be normal because it is expected that viewership is lower at 1:00 am. The process may be traffic loading agnostic in that the process may start with any type of traffic load on a delivery entity. For example, a content delivery network may be provided with an incrementally increasing amount of traffic load after the content delivery network first starts. In some examples, the content delivery network may be provided with 1% of a traffic load at the start, which is increased to 5% of a traffic load after 30 days, which is increased to 50% of a traffic load after another 30 days, etc. until 100% of a traffic load is reached. The anomaly detection process may be used at any amount of traffic load and dynamically learns to detect anomalies as the traffic load is increased. The anomaly detection process may be start-type agnostic in that the model may be used for a cold start, which does not use any previous history of data to a warm start that may use historical data for training. The following will describe the use of weights to enable anomaly detection while data changes over time.
An anomaly detection engine 104 may use a model that detects anomalies in data, such as data that is based on the delivery of content from delivery entities 106 to client devices. As will be described in more detail below, anomaly detection engine 104 uses machine learning to analyze the feedback information received from delivery entities 106 to detect anomalies. The feedback information may be based on the delivery of content from delivery entities 106 to client devices. The model receives values for variables and determines outliers in the data that may be considered anomalies.
The data received may be based on a time series of instances of data. For example, anomaly detection engine 104 receives data at time granularities. In some embodiments, streaming services 102 may receive data and generate time series data sets for every second, minute, hour or any other granularity. Each data set may be associated with a time in the time series. For example, data may be continuously received, and streaming services 102 may generate an instance of data for each minute from the data received in the last minute. This process occurs for each minute to create instances of data for each minute. An anomaly may be data from a first time that is considered an anomaly from other data at other times. As will be described below in more detail, a boundary may be used to determine anomalous data.
Each instance of data may have values for multiple variables associated with different metrics. For example, there may be data for a number of playback attempts, a number of video startup failures, a number of video startup exits, a number of playback failures, etc. These variables may be correlated in that the experience of one of the events may affect the experience of another metric. Anomaly detection engine 104 evaluates the correlations among the different variables and detects outliers from the correlated data. The variables may be interacting with each other. Thus, the interactions between variables are reflected in determining inlier or outlier statuses. If variables are interacting normally, then they are not outliers. If variables are interacting anomalously, then they could be outliers. For example, there may be two variables of viewers and time of day. There may be 10 million viewers at 5:00 μm, and this may be considered inline of expected correlations. However, there may be 10 million viewers at 1:00 am, and this may be considered an outlier of expected correlations. That is, there may be an expected 1 million viewers at 1:00 am because this is a time period when most viewers are sleeping. The correlation is examined for anomalies, rather than just the number of viewers irrespective of time.
Anomaly detection engine 104 may output the anomalies that are detected. For example, anomaly detection engine 104 may output different times in the time series and the variables in which an anomaly is detected. Then, streaming services 102 may perform an action to resolve any problems that have been occurring. For example, the action may be to determine acceptable ranges of values for the variables, and send the ranges to a delivery entity 106 with a notification of the problem. Also, anomaly detection engine 104 may also troubleshoot an issue associated with the anomaly.
The following will now describe the anomaly detection process in more detail.
Anomaly detection engine 104 may monitor each delivery entity 106 separately and perform the anomaly detection process for each delivery entity 106. That is, anomaly detection engine 104 analyzes data for each delivery entity 106 separately for anomalies. In other embodiments, anomaly detection engine 104 may monitor data for multiple delivery entities 106 together for anomalies. However, identification of which delivery entity in which an issue occurred may be required when data is combined for multiple delivery entities 106.
At 204, anomaly detection engine 104 stores the instance of data in a queue and may possibly dequeue other instances of data from the queue. For example, the just received instance of the data for a time is stored in the queue. Then, an instance that was already stored in the queue may be removed from the queue. In some embodiments, a last in, first out process is used where the oldest instance of data is removed when the new instance of data is inserted in the queue (or an equal number of instances is removed when new instances are inserted in the queue). The dequeuing of instances of data may not be needed in some cases. For example, in a cold start process, which will be described in more detail below, the queue may not be full, such as a queue may store one hundred entries and entries are only dequeued after one hundred entries are stored in the queue. As the instances of data are received, anomaly detection engine 104 stores the instances in the queue until the queue becomes full. Thereafter, when a new instance of data is received, the oldest instance of data may be removed from the queue.
At 206, anomaly detection engine 104 may apply weights to the values of the data in the queue. The weighting of the instances may allow anomaly detection engine 104 to apply more weight to instances that may be considered more important. In some embodiments, the weighting may allow anomaly detection engine 104 to prioritize current trends and patterns over outdated ones. For example, when data trends drift or seasonality patterns occur, the weighting may allow anomaly detection engine 104 to detect the data drift or seasonality patterns more quickly by weighting the more recent instances of data higher than the older instances of data. Additionally, the weights may be used when there are pivot points in the data that are detected. For example, when new observations of instances of data significantly deviate from the previous instances of data, anomaly detection engine 104 detects the pivot point and then assigns higher weights to the newer incoming instances of data that are received after the pivot point and lower weights to the older instances of data that are received before the pivot point. This may improve the detection of anomalies by weighting data that may not be relevant to detect anomalies less, and the outdated data may be even can be excluded from the analysis. Examples of applying weights will be described in more detail in
At 208, anomaly detection engine 104 projects the data into a correlation space. The correlation space may be a space that represents the correlation of the multiple variables that are included in the instance of data. In some embodiments, the correlation space may be a higher dimensional space that may represent the correlations of the values of the multiple variables. A point in the correlation space may represent the values from the variables in the instance of data. Each instance of data may be projected into the correlation space as a point. However, an instance of data may be projected into the correlation space as multiple points. The projection will be discussed in more detail in
At 210, anomaly detection engine 104 evaluates the projected instances of data in the correlation space to determine a boundary. For example, a boundary may be a threshold that indicates which instances are acceptable, such as inliers to a boundary. Instances that do not meet the threshold, such as are outside of the boundary may be instances that may be anomalies. The boundary may be determined based on analysis of the points in the correlation space. The boundary determination will be discussed in more detail in
At 212, anomaly detection engine 104 outputs instances of data that are outliers to the boundary. For example, the time in the time series of the instances that are outside of the boundary may be output. Then, streaming services 102 may analyze the outliers and determine any remedial actions that can be performed. The analysis will be discussed in more detail in
The variables may be correlated with each other. For example, the number of playback video startup failures may be correlated with the number of playback attempts. The number of playback attempts may affect the number of playback video startup failures as more playback attempts may cause more playback startup failures. To analyze correlations for anomalies, the correlation among variables may be analyzed for anomalies. The different degrees of correlation between variables is shown at 302 according to some embodiments. The correlation may be expressed from a range of 0.0 to 1.0, where values closer to 1.0 indicate more correlation between variables. For example, playback video startup exits count may be correlated with the video playback failures count with a value of 0.8 because an exit may be based on failures occurring. Also, playback video startup failures count may be correlated with the video playback failures count with a value of 0.6 because failures may result in some failures at startup. Also, a higher correlation may be the number of viewers and the time of day.
To analyze the variables together, the values of the variables may be projected into a space based on the correlation of the variables.
In some embodiments, anomaly detection engine 104 may use a transformation to project the dimensional data from the instances of data into a space. In some embodiments, the process to perform the transformation may be singular value decomposition (SVD). However, other methods may be used. In some embodiments, the space is referred to as a high dimensional space. Anomaly detection engine 104 may use a Principal Component Analysis (PCA) transformation to analyze the data that includes a high number of dimensions and features per time and reduce the dimensionality of the variables in the instance of data. The transformation may linearly transform the data into a new coordinate system where the variation in the data can be described with fewer dimensions than the initial data. Although principal component analysis is described, other data transformation techniques may be used. For example, the transformation does not have to be into a higher dimensional space, but could be to a lower dimensional space.
As shown, inliner points are shown in blue and outliner points are shown in red based on boundary 506. For example, an outlier point outside of boundary 506 is shown at 508 in the color red. An outlier point does not meet a threshold set by boundary 506 (e.g., is outside of boundary 506). Also, an inliner point is shown at 510 in the color of blue. An inliner point may meet the threshold (e.g., is inside of boundary 506). Other inliner points with values within boundary 506 are shown in blue and other outlier points with values outside of boundary 506 are shown in red. Although outside of boundary 506 and inside of boundary 506 is described, the threshold may be applied in other ways, such as inliner points may be less than a threshold and outlier points may be greater than a threshold.
The outlier points may be transformed to be values on boundary 506.
The use of the values on boundary 506 may be to determine a range of values for the outlier points that would be valid.
Anomaly detection engine 104 may improve the analysis of data when certain conditions occur, such as incremental loading and data drift. Anomaly detection engine 104 may weight instances of data in the queue to account for different patterns that may occur over time, such as based on incremental loading and data drift. The use of the weights may be useful in content delivery, which may often experience data drift or incremental loading scenarios. The data drift may occur as viewing patterns of content change. Anomaly detection engine 104 is able to adapt to the data drift in real-time. Also, anomaly detection engine 104 can be used in an incremental load scenario with having to wait to perform retraining with a large batch of data. Rather, anomaly detection engine 104 learns the new patterns to detect anomalies as load increases (or decreases).
In some embodiments, delivery entities 106 may be incrementally loaded, which causes differences in values for variables. For example, an incremental load provides incrementally more traffic (e.g., load) for a delivery entity 106 to process over time. That is, delivery entity 106 may deliver more content over time. In some embodiments, an incremental learning approach to discover the trending and expected behavior may be used as the growth, or a decline, of the use of a delivery entity.
To account for the incremental loading process, anomaly detection engine 104 may weight the values in a queue differently. Also, anomaly detection engine 104 may use a dequeuing process to remove some instances of data as new instances of data are received. In some embodiments, anomaly detection engine 104 may weight the more recent instances of data higher than the older instances of data. This may put more emphasis on the more recent patterns compared to the older patterns. Emphasizing the more recent data in an incremental loading process may improve the anomaly detection because as the traffic load increases, different patterns may be experienced with the increased load. For example, in the launch phase, the value of the variables is relatively constant around a value of 1. Also, the growth phase, the value of the variables is relatively constant around a value of 2 or 3. Then, at the end of the growth phase, the value of the variables starts a new pattern where the data fluctuates much more around a value of 10 in graph 800-1 or a value of 5 in graph 800-2. The data may fluctuate even more in the mature phase.
By weighting the more recent instances of data that are received, the point in the space may better represent the current conditions. When instances of data are weighted higher, the corresponding points in the correlation space may affect how the boundary is drawn. For example, if points that are weighted higher are in one area of the space, then the boundary may be moved to encompass that area of the space more or be closer to that area of the space compared to another area of the space where the points are weighted lower. The weights may be expressed in different ways. For example, the weight can be expressed as an exponential decay function or be based on the traffic ratio that is used. For example, the weight may decay as the instances of data become older. Also, an instance while the traffic ratio is 1% may have a smaller weight than an instance while the traffic ratio is 50%.
The above higher weighting of more current instances compared to older instances may also useful as data drifts. Anomaly detection engine 104 may also detect when change points occur in the instances of data. This may be helpful to detect abrupt changes in patterns rather than more subtle changes in data. That is, when data drifts into a new pattern, the higher weights to more recent instances of data may apply more weight to newer instances and help find anomalies to the new pattern faster.
Anomaly detection engine 104 may adjust the weights based on different detections of change points. For example, when a change point occurs, anomaly detection engine 104 may weight the instances of data before the change point less than the instances of data after the change point. For example, for graph 1000-1 and the variable for a graph 1000-2, the instances of data after change point 1002 may be weighted greater than the instances of data before change point 1002. Also, the instances of data after change point 1004 may be weighted greater than the instances before change point 1004 for graphs 1000-1 and 1000-2. The weights may use a different weighting method compared to the last in first out weighting. For example, the weights that are used before the change point may all be weighted lower than the weights after the change point, such as 10 percent weight for all instances before the change point and 90 percent weight for all instances after the change point.
Additionally, it can be seen that the values in graph 1000-3 do not change before change point 1004. Anomaly detection engine 104 may also weight the different variables differently. For example, if one variable is detected as being more important than another variable, then that variable may be weighted more. This may change the position of the point in this space based on the value of the variables that are weighted. Accordingly, anomaly detection engine 104 may determine that graph the variables for graph 1000-1 and 1000-2 may be weighted higher than the variable for graph 1000-3 because the graph for 1000-1 and 1000-2 may provide more information on the detection of anomalies because they have changed, but data in graph 1000-3 has not changed significantly. Accordingly, anomaly detection engine 104 may weight the variables for graph 1000-1 and 1000-2 higher than the variable for graph 1000-3.
The use of the weights may be useful in content delivery. Content delivery may experience data drift or incremental loading scenarios. The data drift may occur as viewing patterns of content change. Anomaly detection engine 104 is able to adapt to the data drift in real-time. Also, anomaly detection engine 104 can be used in an incremental load scenario with having to perform retraining with a batch of data. Rather, anomaly detection engine 104 learns the new patterns as load increases (or decreases).
The use of weights may be used when a cold start is used. A cold start may be where a model for anomaly detection engine 104 is used without considering any historical data. As the model learns which instances of data are anomalies, the weights can be used to assign higher weights to newer instances or instances after a change point occurs. This allows the dynamic retraining of the model. A warm start may also be similarly used. The model may be trained with some training data (e.g., historical data). Then, the model will dynamically learn how to detect anomalies as data changes as described in the cold start case.
The computations may be combined to parallelize the computation. For example, the covariances of two or more sets may be combined to paralyze the computation. That is, multiple instances of data may be combined into a single point in the space. This may improve the speed of the calculation.
Accordingly, anomaly detection engine 102 may perform anomaly detection without the need for a large amount of historical data for training. For example, no historical data may be used to perform the analysis for a cold start. The training of the model of anomaly detection engine 104 may also be ongoing as new data is received, which allows anomaly detection engine 104 to adjust in incremental loading cases or when data drifts. For example, the weights may be used to emphasize newer correlations or patterns as they occur.
Features and aspects as disclosed herein may be implemented in conjunction with a video streaming system 1100 in communication with multiple client devices via one or more communication networks as shown in
In one embodiment, a media program provider may include a library of media programs. For example, the media programs may be aggregated and provided through a site (e.g., website), application, or browser. A user can access the media program provider's site or application and request media programs. The user may be limited to requesting only media programs offered by the media program provider.
In system 1100, video data may be obtained from one or more sources for example, from a video source 1110, for use as input to a video content server 1102. The input video data may comprise raw or edited frame-based video data in any suitable digital format, for example, Moving Pictures Experts Group (MPEG)-1, MPEG-2, MPEG-4, VC-1, H.264/Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC), or other format. In an alternative, a video may be provided in a non-digital format and converted to digital format using a scanner or transcoder. The input video data may comprise video clips or programs of various types, for example, television episodes, motion pictures, and other content produced as primary content of interest to consumers. The video data may also include audio or only audio may be used.
The video streaming system 1100 may include one or more computer servers or modules 1102, 1104, and 1107 distributed over one or more computers. Each server 1102, 1104, 1107 may include, or may be operatively coupled to, one or more data stores 1109, for example databases, indexes, files, or other data structures. A video content server 1102 may access a data store (not shown) of various video segments. The video content server 1102 may serve the video segments as directed by a user interface controller communicating with a client device. As used herein, a video segment refers to a definite portion of frame-based video data, such as may be used in a streaming video session to view a television episode, motion picture, recorded live performance, or other video content.
In some embodiments, a video advertising server 1104 may access a data store of relatively short videos (e.g., 10 second, 30 second, or 60 second video advertisements) configured as advertising for a particular advertiser or message. The advertising may be provided for an advertiser in exchange for payment of some kind or may comprise a promotional message for the system 1100, a public service message, or some other information. The video advertising server 1104 may serve the video advertising segments as directed by a user interface controller (not shown).
The video streaming system 1100 also may include anomaly detection engine 104.
The video streaming system 1100 may further include an integration and streaming component 1107 that integrates video content and video advertising into a streaming video segment. For example, streaming component 1107 may be a content server or streaming media server. A controller (not shown) may determine the selection or configuration of advertising in the streaming video based on any suitable algorithm or process. The video streaming system 1100 may include other modules or units not depicted in
The video streaming system 1100 may connect to a data communication network 1112. A data communication network 1112 may comprise a local area network (LAN), a wide area network (WAN), for example, the Internet, a telephone network, a wireless network 1114 (e.g., a wireless cellular telecommunications network (WCS)), or some combination of these or similar networks.
One or more client devices 1120 may be in communication with the video streaming system 1100, via the data communication network 1112, wireless network 1114, or another network. Such client devices may include, for example, one or more laptop computers 1120-1, desktop computers 1120-2, “smart” mobile phones 1120-3, tablet devices 1120-4, network-enabled televisions 1120-5, or combinations thereof, via a router 1118 for a LAN, via a base station 1117 for wireless network 1114, or via some other connection. In operation, such client devices 1120 may send and receive data or instructions to the system 1100, in response to user input received from user input devices or other input. In response, the system 1100 may serve video segments and metadata from the data store 1109 responsive to selection of media programs to the client devices 1120. Client devices 1120 may output the video content from the streaming video segment in a media player using a display screen, projector, or other video output device, and receive user input for interacting with the video content.
Distribution of audio-video data may be implemented from streaming component 1107 to remote client devices over computer networks, telecommunications networks, and combinations of such networks, using various methods, for example streaming. In streaming, a content server streams audio-video data continuously to a media player component operating at least partly on the client device, which may play the audio-video data concurrently with receiving the streaming data from the server. Although streaming is discussed, other methods of delivery may be used. The media player component may initiate play of the video data immediately after receiving an initial portion of the data from the content provider. Traditional streaming techniques use a single provider delivering a stream of data to a set of end users. High bandwidth and processing power may be required to deliver a single stream to a large audience, and the required bandwidth of the provider may increase as the number of end users increases.
Streaming media can be delivered on-demand or live. Streaming enables immediate playback at any point within the file. End-users may skip through the media file to start playback or change playback to any point in the media file. Hence, the end-user does not need to wait for the file to progressively download. Typically, streaming media is delivered from a few dedicated servers having high bandwidth capabilities via a specialized device that accepts requests for video files, and with information about the format, bandwidth, and structure of those files, delivers just the amount of data necessary to play the video, at the rate needed to play it. Streaming media servers may also account for the transmission bandwidth and capabilities of the media player on the destination client. Streaming component 1107 may communicate with client device 1120 using control messages and data messages to adjust to changing network conditions as the video is played. These control messages can include commands for enabling control functions such as fast forward, fast reverse, pausing, or seeking to a particular part of the file at the client.
Since streaming component 1107 transmits video data only as needed and at the rate that is needed, precise control over the number of streams served can be maintained. The viewer will not be able to view high data rate videos over a lower data rate transmission medium. However, streaming media servers (1) provide users random access to the video file, (2) allow monitoring of who is viewing what video programs and how long they are watched (3) use transmission bandwidth more efficiently, since only the amount of data required to support the viewing experience is transmitted, and (4) the video file is not stored in the viewer's computer, but discarded by the media player, thus allowing more control over the content.
Streaming component 1107 may use TCP-based protocols, such as HyperText Transfer Protocol (HTTP) and Real Time Messaging Protocol (RTMP). Streaming component 1107 can also deliver live webcasts and can multicast, which allows more than one client to tune into a single stream, thus saving bandwidth. Streaming media players may not rely on buffering the whole video to provide random access to any point in the media program. Instead, this is accomplished using control messages transmitted from the media player to the streaming media server. Other protocols used for streaming are HTTP live streaming (HLS) or Dynamic Adaptive Streaming over HTTP (DASH). The HLS and DASH protocols deliver video over HTTP via a playlist of small segments that are made available in a variety of bitrates typically from one or more content delivery networks (CDNs). This allows a media player to switch both bitrates and content sources on a segment-by-segment basis. The switching helps compensate for network bandwidth variances and infrastructure failures that may occur during playback of the video.
The delivery of video content by streaming may be accomplished under a variety of models. In one model, the user pays for the viewing of video programs, for example, paying a fee for access to the library of media programs or a portion of restricted media programs, or using a pay-per-view service. In another model widely adopted by broadcast television shortly after its inception, sponsors pay for the presentation of the media program in exchange for the right to present advertisements during or adjacent to the presentation of the program. In some models, advertisements are inserted at predetermined times in a video program, which times may be referred to as “ad slots” or “ad breaks.” With streaming video, the media player may be configured so that the client device cannot play the video without also playing predetermined advertisements during the designated ad slots.
Referring to
A bus 1214 or other communication component may support communication of information within the apparatus 1200. The processor 1202 may be a specialized or dedicated microprocessor configured or operable to perform particular tasks in accordance with the features and aspects disclosed herein by executing machine-readable software code defining the particular tasks. Processor memory 1204 (e.g., random access memory (RAM) or other dynamic storage device) may be connected to the bus 1214 or directly to the processor 1202, and store information and instructions to be executed by a processor 1202. The memory 1204 may also store temporary variables or other intermediate information during execution of such instructions.
A computer-readable medium in a storage device 1224 may be connected to the bus 1214 and store static information and instructions for the processor 1202; for example, the storage device (CRM) 1224 may store the modules for operating system 1206, browser 1208, and media player 1210 when the apparatus 1200 is powered off, from which the modules may be loaded into the processor memory 1204 when the apparatus 1200 is powered up. The storage device 1224 may include a non-transitory computer-readable storage medium holding information, instructions, or some combination thereof, for example instructions that when executed by the processor 1202, cause the apparatus 1200 to be configured or operable to perform one or more operations of a method as described herein.
A network communication (comm.) interface 1216 may also be connected to the bus 1214. The network communication interface 1216 may provide or support two-way data communication between the apparatus 1200 and one or more external devices, e.g., the streaming system 1100, optionally via a router/modem 1226 and a wired or wireless connection 1225. In the alternative, or in addition, the apparatus 1200 may include a transceiver 1218 connected to an antenna 1229, through which the apparatus 1200 may communicate wirelessly with a base station for a wireless communication system or with the router/modem 1226. In the alternative, the apparatus 1200 may communicate with a video streaming system 1100 via a local area network, virtual private network, or other network. In another alternative, the apparatus 1200 may be incorporated as a module or component of the system 1100 and communicate with other components via the bus 1214 or by some other modality.
The apparatus 1200 may be connected (e.g., via the bus 1214 and graphics processing unit 1220) to a display unit 1228. A display 1228 may include any suitable configuration for displaying information to an operator of the apparatus 1200. For example, a display 1228 may include or utilize a liquid crystal display (LCD), touchscreen LCD (e.g., capacitive display), light emitting diode (LED) display, projector, or other display device to present information to a user of the apparatus 1200 in a visual display.
One or more input devices 1230 (e.g., an alphanumeric keyboard, microphone, keypad, remote controller, game controller, camera, or camera array) may be connected to the bus 1214 via a user input port 1222 to communicate information and commands to the apparatus 1200. In selected embodiments, an input device 1230 may provide or support control over the positioning of a cursor. Such a cursor control device, also called a pointing device, may be configured as a mouse, a trackball, a track pad, touch screen, cursor direction keys or other device for receiving or tracking physical movement and translating the movement into electrical signals indicating cursor movement. The cursor control device may be incorporated into the display unit 1228, for example using a touch sensitive screen. A cursor control device may communicate direction information and command selections to the processor 1202 and control cursor movement on the display 1228. A cursor control device may have two or more degrees of freedom, for example allowing the device to specify cursor positions in a plane or three-dimensional space.
Some embodiments may be implemented in a non-transitory computer-readable storage medium for use by or in connection with the instruction execution system, apparatus, system, or machine. The computer-readable storage medium contains instructions for controlling a computer system to perform a method described by some embodiments. The computer system may include one or more computing devices. The instructions, when executed by one or more computer processors, may be configured or operable to perform that which is described in some embodiments.
As used in the description herein and throughout the claims that follow, “a,” “an”, and “the” includes plural references unless the context clearly dictates otherwise. Also, as used in the description herein and throughout the claims that follow, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
The above description illustrates various embodiments along with examples of how aspects of some embodiments may be implemented. The above examples and embodiments should not be deemed to be the only embodiments and are presented to illustrate the flexibility and advantages of some embodiments as defined by the following claims. Based on the above disclosure and the following claims, other arrangements, embodiments, implementations, and equivalents may be employed without departing from the scope hereof as defined by the claims.
