Patent Grant
11659255
Automatic Content Recognition (ACR) systems provide information about content being displayed at a particular point in time by a specific media display device. An ACR system may be implemented as a video matching system. Generally, a video matching system may operate by obtaining data samples from known video data sources, generating identification information from these samples, and storing identification information, along with known information about the video, in a database. A particular media device can take data samples from an unknown video being displayed, generate identification information from the samples, and attempt to match the identification information with identification information stored in the database. When a match is found, the particular media device can receive, from the database, the information that is known about the video. Generally, the matching operations conducted by the media device can be conducted in real time, that is, as the video is being displayed on the device.
Provided are systems, methods, and computer-program products for identifying a media content stream when the media content stream is playing an unscheduled, common media segment. In various implementations, a computing device may be configured to identify media content being played by a media display device at a particular time. The computing device may be configured to implement a video matching system. The computing device may receive a plurality of media content streams, where at least two of the plurality of media content streams concurrently includes a same unscheduled media segment. The computing device may be configured to determining that the media display device is playing the unscheduled media segment at a current time. To make this determination, the computing device may examine the media content available at the current time in each of the plurality of media content streams. The computing device may further be configured to determine identification information from the media content included in the media content stream that is being played by the media display device at the current time. The identification information may identify the media content stream. The computing device may further determine contextually-related content. The contextually-related content may be disabled while the unscheduled media segment is being played by the media display device. The computing device may further be configured to display the media content stream and the contextually-related content after the unscheduled media segment has been played.
In various implementations, the contextually-related content is selected using the identification information that identifies the media content stream. The contextually-related content may be provided to the media display device.
In various implementations, identifying the media content stream may include detecting a graphic superimposed onto the unscheduled media segment while the unscheduled media segment is being played by the media display device. The graphic may provide additional identification information for the media content stream.
In various implementations, media content used to determine the identification information may include media content included in the media content stream before or after the unscheduled media segment.
In various implementations, the computing device may be configured to determine that the media display device has been playing the unscheduled media segment since a beginning of the unscheduled media segment. In these implementations, the computing device may identify the media content stream using identification information determined for media content included in the media content stream prior to the unscheduled media segment.
In various implementations, the computing device may be configured to determine that the media display device has been playing the unscheduled media segment since a point after a beginning of the unscheduled media segment. In these implementations, the computing device may identify the media content stream using identification information for media content included in the media content stream after the unscheduled media segment.
Illustrative embodiments are described in detail below with reference to the following figures:
Automatic Content Recognition (ACR) systems provide information about content being displayed at a particular point in time by a specific media display device. For example, an ACR system can provide information such as the channel being viewed, the title of the video, some text identifying the video or the content of the video, the portion of the video being viewed, one or more categories for the video, the author and/or producer of the video, and so on. This information can subsequently be used, for example, to provide viewership statistics for the video (e.g., how frequently the video has been viewed by how many people, at what times, etc.) and/or to suggest targeted content for the viewer, such as advertising or interactive content.
An ACR system may be implemented as a video matching system. Generally, a video matching system may operate by obtaining data samples from known video data sources, generating identification information from these samples, and storing identification information, along with known information about the video, in a database. A particular media device can use a similar process to identify unknown video content. Specifically, the media device can take data samples from an unknown video being displayed, generate identification information from the samples, and attempt to match the identification information with identification information stored in the database. When a match is found, the particular media device can receive, from the database, the information that is known about the video. Generally, the matching operations conducted by the media device can be conducted in real time, that is, as the video is being displayed on the device.
A video matching system as described above may, however, have difficulty identifying the media content being displayed by a media device when the same content is being displayed on multiple channels at the same time. Furthermore, the system may not be able to determine, for example, which channel the viewer is watching, or what contextually-related information to provide.
One example of multiple channels displaying the same content at the same time occurs when multiple local televisions stations provide syndicated content to support “breaking news” stories. For example, a national broadcast agency (e.g., American Broadcasting Company (ABC), Columbia Broadcasting System (CBS), National Broadcasting Company (NBC), Fox Network, Cable News Network (CNN), etc.) may provide a video feed when political speeches, natural disasters, or human-made incidents occur. In this example, multiple national and/or local broadcast channels may pick up the video feed as it is being broadcast by the national broadcaster, and may re-broadcast the feed to local viewers. As result, multiple channels may be displaying identically video content at the same time. The same situation may occur in other contexts, such as when multiple channels display the same commercial or sporting event at the same time.
As noted above, a video matching system may rely on data samples collected from known video sources, such as local and national channels, where programming information may provide information such as the title or other identifying string for video content, and other information about the video content. When multiple channels display the same content, however, the video matching system may not be able to uniquely identify the video content. For example, the video matching system may associate one video with two or more channels. Subsequently, should a media device tune into one of the channels that is carrying the same content, the video matching system may not be able to determine which channel the media device has tuned into.
In various implementations, a video matching system may be configured to improve the accuracy of automated content recognition in the presence of ambiguity caused by a common video segment appearing simultaneously on multiple channels. Without accuracy improvements, the presence of common video segments displaying simultaneously on multiple channels being monitored may cause ambiguity in identifying content being displayed by a media device. In various implementations, the video matching system may use information from media content displayed on the media display device before and/or after the media device began to display the common video segment. Using this information, the video matching system is able to attach identification information to samples taken from the common video segment.
On national and local channels that may at some point display a common video segment, the channel may provide some uniquely identifiable content prior to displaying the common video segment. In various implementations, this unique content can be used to assist in identifying the common video segment. For example, in certain broadcast news, there are parts of the news program that are known to be from the local channel. For example, the news program may introduce and/or comment on the common video segment before the common video segment is displayed. The introductory segment may be referred to as “talking heads” segment, that is, a segment where two or more people are seated behind a desk and are framed in the video from the center chest up. When a “talking heads” segment is detected, a video matching system can, for example, add a new timeline signal or events that can be used to assist in identifying the common video segment.
In various implementations, a media display device that is configured to use the video matching system may obtain and track timeline signals or events, such as those that may be generated for “talking heads” segments. In these implementations, the media device may check for a timeline event when the media device encounters an unknown and possibly common video segment. In one case, the media device may not find a timeline event, which may indicate to the media device that the device has just been turned on or tuned in to the channel displaying the common video content. In this case, the data collection process of the media device may be configured to avoid using the common video segment for identification purposes. In other cases, the media device may use timeline events received before and/or after the common video segment to generate identification information for the common video segment.
In various implementations, the video matching may also use timeline events to more rapidly identify commercials. Generally, the amount of time a video matching system takes to obtain sufficient samples to match a known commercial with a high-degree of probability may be referred to as the “commercial confidence interval.” Using the techniques described herein, the commercial confidence interval may be reduced when the media device uses timeline events within a specified window of past time.
I. Audio-Video Content
In various implementations, a video content matching system may be configured to identify media content being displayed by a media display device. In various implementations, the video content system can be used to provide contextually targeted content to a media display devices, where the targeted content is selected based the identified media content.
Media content includes video, audio, text, graphics, tactile representations of visual and/or audible data, and various combinations of visual, audible, or tactile information. For example, media content can include synchronized audio and video, such as movies or television shows. As another example, media content can include text and graphics, such as web pages. As another example, media content can include photographs and music, such as a photo slide show with a soundtrack.
A media display device can be a device capable of displaying various media content. Media display devices may include, for example, televisions systems. Television (TV) systems include, for example, televisions such as web TVs and connected TVs (also known as “Smart TVs”), and optionally equipment incorporated in or co-located with the TV, such as set-top boxes (STB), a digital video disc (DVD) players, and/or digital video recorders (DVR).
Connected TVs are TVs that are connected to a network, such as the Internet. In various implementations, a network connected TV may be connected to a local wired or wireless network, such as for example in a private home or in a business office. A Connected TV can run an application platform such as Google's Android, or some other platform configured to provide interactive, smartphone or tablet-like software applications, which also may be referred to as “apps.”
In various implementations, a media display device may receive signals, such as television signals. Televisions signals include, for example, signals representing video and audio data, broadcast together and synchronized for simultaneous display. For example, television signals may include television programs and/or commercials. In some cases, television signals may include additional information relating to the audio-video content in a television signals. This additional data may be referred to as “metadata.” The term “metadata” may also be used to describe information that is associated with video or audio-video content transmitted other than as television signals (e.g. transmitted as digitized and/or packetized data over a network). Metadata may include information about the content, such as information identifying the content, a description of the content, one or more categories for the content, an author and/or publisher of the content, and so on. Because metadata is transmitted with the content that the metadata is associated with, the metadata can be used to provide information about the content as the content is being viewed or played.
Not all media display devices have access to metadata. Hence, not all media display devices are able to determine what they are displaying or playing at any given moment. Without this information, a media display device may not be able to provide customized or individualized content or advertisements for a specific viewer. While some information about content being provide to a media display device may be available in the distribution pipeline, this information may be lost or removed by the time the content arrives at the media display device.
In some implementations, metadata may be provided with audio-video content using various methods. For example, in some implementations, identification information may be encoded into the content using watermarks. In these implementations, the identification information may be encoded so that the information is not lost when the content is compressed for transmission and decompressed for display. Such methods, however, may require that a receiving media display device be able to extract the identification information from the content. Additionally, these methods may not enable up-to-the-moment identification of a particular video being played, with fraction-of-a-second identification capability.
In various implementations, advancements in fiber optic and digital transmission technology have enabled the media industry to provide a large channel capacity, where “channels” include traditional broadcast channels, satellite signals, digital channels, streaming content, and/or user-generated content. In some cases, media providers such as satellite systems may be referred to as Multichannel Video Programming Distributors (MVPD). In some implementations, media providers are also able to use the increase data capacity of modern transmission systems to provide some of interactive content, such as interactive television (ITV). The increased processing power of Smart TVS, set-top boxes, and similar devices may further enable interactive content.
Interactive television may enable television systems to serve as a two-way information distribution mechanism in a manner similar to the World Wide Web. Interactive televisions may provide a variety of marketing, entertainment, and educational capabilities, such as for example enabling a viewer to order an advertised product or service, compete against contestants in a game show, participate in a live classroom session, and so on. In some implementations, the interactive functionality may be controlled by a set-top box. In these implementations, the set-top box may execute an interactive program associated with video content, such as a TV broadcast. Interactive functionality may be displayed on the TV's screen and may include icons or menus to allow a viewer to make selections via the TV's remote control or a keyboard.
In various implementations, interactive content may be incorporated into audio-video content. In some implementations, the audio-video content may consist of a broadcast stream. A broadcast stream may also be referred to as a “channel” or a “network feed”. The term “broadcast stream” may refer to a broadcast signal received by a television over, for example, an antenna, a satellite, a coaxial cable, a digital subscriber line (DSL) cable, a fiber optic cable, or some other transmission medium. In various implementations, interactive content may be incorporated into a audio-video content using “triggers.” Triggers may be inserted into the content for a particular program. Content that includes triggers may be referred to as “enhanced program content” or an “enhanced television program” or and “enhanced video signal.” Triggers may be used to alert the media display device (e.g., at a set-top box or a the processor in a Smart TV) that interactive content is available. The trigger may contain information about available content as well as where the interactive content can be found (e.g., a memory address, a network address, and/or a website address). A trigger may also contain information that can be displayed on the media display device to the viewer. For example, information provided by the trigger, may be displayed at the bottom of a screen provided by the media display device. The displayed information may prompt the viewer to perform some action or choose amongst multiple of options.
II. Video Matching
In various implementations, a video content system may be configured to identify media content that is being displayed or played by a media display device. In various implementations, information identifying the content being viewed at a particular moment in time can be used to capture and appropriately respond to a viewer's specific reaction, such as requesting that the content be rewound or requesting that a video be restarted from its beginning. Alternatively or additionally, the identification information can be used to trigger targeted content, such as advertisements, which may be provided by the content provider or an advertiser. Information identifying audio-video content can thus be used to provide viewer-customized, video-on-demand (VoD) capabilities to devices that otherwise do not have Smart TV capabilities.
In various implementations, a video segment be identified by sampling, at periodic intervals, a subset of the pixel data being displayed on the screen of a media display device, and then finding similar pixel data in a content database. In some implementations, a video segment may be identified by extracting audio data associated with the video segment and finding similar audio data in a content database. In some implementations, a video segment may be identified by processing the audio data associated with the video segment using automated speech recognition techniques, and searching text transcriptions from known video content to locate matching text phrases. In some implementations, a video segment may be identified by processing metadata associated with the video segment.
In various implementations, a video matching system may be used to provide contextually targeted content to an interactive media display system. The contextually targeted content may be based on identification of a video segment being displayed, and also on the time at which the video segment is being played (day or evening, 3:00 in the afternoon, etc.) and/or the portion of the video segment that is currently being displayed (e.g., a current offset from the beginning of the video). Here, “playing time” and “offset time” may be used interchangeably to describe the part of a video that is currently being displayed.
In various implementations, a media display device equipped with a content matching system, having identified content that is presently being displayed or played by the media display device, may be able to deduce the subject matter of the content, and interact with the viewer accordingly. For example, the media display device may be able to provide instant access to video-on-demand versions of content, and/or to higher resolutions or 3D formats of the content. Additionally, the media display device may provide the ability to start over, fast forward, pause and re-wind the content. In various implementations, advertising can be included in the content. In these implementations, some or all advertising messages can be customized, such as for example to viewer's geographic location, demographic group, or shopping history. Alternatively or additionally, advertisements can be reduced in number or length, or be eliminated entirely.
In various implementations, once a video segment is identified, the offset time may be determined by sampling a subset of the pixel data (or associated audio data) being displayed or played by a media display device, and finding similar pixel (or audio) data in a content database. In various implementations, the offset time can be determined by extracting audio or image data associated with such video segment and finding similar audio or image data in a content database. In various implementations, the offset time can be determined by processing the audio data associated with such video segment using automated speech recognition techniques. In various implementations, the offset time can determined by processing metadata associated with such video segment.
In various implementations, a system for video matching can be included in a television system. In various implementations, the television system includes a connected TV. In various implementations, a video matching system may be included in part in a connected television and in part on a server connected to the connected television over the Internet.
The matching system 100 includes a client device 102 and a matching server 104. The client device 102 includes a media client 106, an input device 108, an output device 110, and one or more contextual applications 126. The media client 106 (which can include a television system, a computer system, or other electronic device capable of connecting to the Internet) can decode data (e.g., broadcast signals, data packets, or other frame data) associated with video programs 128. The media client 106 can place the decoded contents of each frame of the video into a video frame buffer in preparation for display or for further processing of pixel information of the video frames. The client device 102 can be any electronic decoding system that can receive and decode a video signal. The client device 102 can receive video programs 128 and store video information in a video buffer (not shown). The client device 102 can process the video buffer information and produce unknown data points (which can referred to as “cues”), described in more detail below with respect to
The input device 108 can include any suitable device that allows a request or other information to be input to the media client 106. For example, the input device 108 can include a keyboard, a mouse, a voice-recognition input device, a wireless interface for receiving wireless input from a wireless device (e.g., from a remote controller, a mobile device, or other suitable wireless device), or any other suitable input device. The output device 110 can include any suitable device that can present or otherwise output information, such as a display, a wireless interface for transmitting a wireless output to a wireless device (e.g., to a mobile device or other suitable wireless device), a printer, or other suitable output device.
The matching system 100 can begin a process of identifying a video segment by first collecting data samples from known video data sources 118. For example, the matching server 104 collects data to build and maintain a reference database 116 from a variety of video data sources 118. The video data sources 118 can include media providers of television programs, movies, or any other suitable video source. Video data from the video data sources 118 can be provided as over-the-air broadcasts, as cable TV channels, as streaming sources from the Internet, and from any other video data source. In some examples, the matching server 104 can process the received video from the video data sources 118 to generate and collect reference video data points in the reference database 116, as described below. In some examples, video programs from video data sources 118 can be processed by a reference video program ingest system (not shown), which can produce the reference video data points and send them to the reference database 116 for storage. The reference data points can be used as described above to determine information that is then used to analyze unknown data points.
The matching server 104 can store reference video data points for each video program received for a period of time (e.g., a number of days, a number of weeks, a number of months, or any other suitable period of time) in the reference database 116. The matching server 104 can build and continuously or periodically update the reference database 116 of television programming samples (e.g., including reference data points, which may also be referred to as cues or cue values). In some examples, the data collected is a compressed representation of the video information sampled from periodic video frames (e.g., every fifth video frame, every tenth video frame, every fifteenth video frame, or other suitable number of frames). In some examples, a number of bytes of data per frame (e.g., 25 bytes, 50 bytes, 75 bytes, 100 bytes, or any other amount of bytes per frame) are collected for each program source. Any number of program sources can be used to obtain video, such as 25 channels, 50 channels, 75 channels, 100 channels, 200 channels, or any other number of program sources. Using the example amount of data, the total data collected during a 24-hour period over three days becomes very large. Therefore, reducing the number of actual reference data point sets is advantageous in reducing the storage load of the matching server 104.
The media client 106 can send a communication 122 to a matching engine 112 of the matching server 104. The communication 122 can include a request for the matching engine 112 to identify unknown content. For example, the unknown content can include one or more unknown data points and the reference database 116 can include a plurality of reference data points. The matching engine 112 can identify the unknown content by matching the unknown data points to reference data in the reference database 116. In some examples, the unknown content can include unknown video data being presented by a display (for video-based ACR), a search query (for a MapReduce system, a Bigtable system, or other data storage system), an unknown image of a face (for facial recognition), an unknown image of a pattern (for pattern recognition), or any other unknown data that can be matched against a database of reference data. The reference data points can be derived from data received from the video data sources 118. For example, data points can be extracted from the information provided from the video data sources 118 and can be indexed and stored in the reference database 116.
The matching engine 112 can send a request to the candidate determination engine 114 to determine candidate data points from the reference database 116. A candidate data point can be a reference data point that is a certain determined distance from the unknown data point. In some examples, a distance between a reference data point and an unknown data point can be determined by comparing one or more pixels (e.g., a single pixel, a value representing group of pixels (e.g., a mean, an average, a median, or other value), or other suitable number of pixels) of the reference data point with one or more pixels of the unknown data point. In some examples, a reference data point can be the certain determined distance from an unknown data point when the pixels at each sample location are within a particular pixel value range.
In one illustrative example, a pixel value of a pixel can include a red value, a green value, and a blue value (in a red-green-blue (RGB) color space). In such an example, a first pixel (or value representing a first group of pixels) can be compared to a second pixel (or value representing a second group of pixels, where the second group of pixels is located in the same display buffer position as the first group of pixels) by comparing the corresponding red values, green values, and blue values respectively, and ensuring that the values are within a certain value range (e.g., within 0-5 values). For example, the first pixel can be matched with the second pixel when (1) a red value of the first pixel is within 5 values in a 0-255 value range (plus or minus) of a red value of the second pixel, (2) a green value of the first pixel is within 5 values in a 0-255 value range (plus or minus) of a green value of the second pixel, and (3) a blue value of the first pixel is within 5 values in a 0-255 value range (plus or minus) of a blue value of the second pixel. In such an example, a candidate data point is a reference data point that is an approximate match to the unknown data point, leading to multiple candidate data points (related to different media segments) being identified for the unknown data point. The candidate determination engine 114 can return the candidate data points to the matching engine 112.
For a candidate data point, the matching engine 112 can add a token into a bin that is associated with the candidate data point and that is assigned to an identified video segment from which the candidate data point is derived. A corresponding token can be added to all bins that correspond to identified candidate data points. As more unknown data points (corresponding to the unknown content being viewed) are received by the matching server 104 from the client device 102, a similar candidate data point determination process can be performed, and tokens can be added to the bins corresponding to identified candidate data points. Only one of the bins corresponds to the segment of the unknown video content being viewed, with the other bins corresponding to candidate data points that are matched due to similar data point values (e.g., having similar pixel color values), but that do not correspond to the actual segment being viewed. The bin for the candidate video content segment that corresponds to the unknown video segment being viewed will have more tokens assigned to it than other bins for segments that do not correspond to the unknown video segment. For example, as more unknown data points are received, a larger number of reference data points that correspond to the bin are identified as candidate data points, leading to more tokens being added to the bin. Once a bin includes a particular number of tokens—that is, the bin reaches a predetermined threshold—the matching engine 112 can determine that the video segment associated with the bin is currently being displayed on the client device 102. A video segment can include an entire video program or a portion of the video program. For example, a video segment can be a video program, a scene of a video program, one or more frames of a video program, or any other portion of a video program.
In determining candidate data points 206 for an unknown data point (e.g., unknown data content 202), the candidate determination engine 214 determines a distance between the unknown data point and the reference data points 204 in the reference database. The reference data points that are a certain distance from the unknown data point are identified as the candidate data points 206. In some examples, a distance between a reference data point and an unknown data point can be determined by comparing one or more pixels of the reference data point with one or more pixels of the unknown data point, as described above with respect to
An example allocation of pixel patches (e.g., pixel patch 304) is shown in
A mean value (or an average value in some cases) of each pixel patch is taken, and a resulting data record is created and tagged with a time code (or time stamp). For example, a mean value is found for each 10×10 pixel patch array, in which case twenty-four bits of data per twenty-five display buffer locations are produced for a total of 600 bits of pixel information per frame. In one example, a mean of the pixel patch 304 is calculated, and is shown by pixel patch mean 308. In one illustrative example, the time code can include an “epoch time,” which representing the total elapsed time (in fractions of a second) since midnight, Jan. 1, 1970. For example, the pixel patch mean 308 values are assembled with a time code 412. Epoch time is an accepted convention in computing systems, including, for example, Unix-based systems. Information about the video program, known as metadata, is appended to the data record. The metadata can include any information about a program, such as a program identifier, a program time, a program length, or any other information. The data record including the mean value of a pixel patch, the time code, and metadata, forms a “data point” (also referred to as a “cue”). The data point 310 is one example of a reference video data point.
A process of identifying unknown video segments begins with steps similar to creating the reference database. For example,
As shown in
III. Common Video Segments
In various implementations, the video matching system may include a matching server 509. The matching server 509 may recognize media being displayed or played by the interactive television system 501. To provide video matching services, the matching server 509 may receive known media cue data 517 from an ingest server 520. The ingest server 520 may obtain data from various known sources, such as Video-on-Demand (VoD) content feeds 515a, local channel feeds 515b, and national channel feeds 515c. Each of these known sources may provide media data (e.g., video and/or audio) as well as information identifying the media data, such as programming guides or metadata. In various implementations, the ingest server 520 may generate known media cue data 517 from the media data received from the various sources. The ingest server 520 may provide the known media cue data 517 to various recipients, including the matching server 509.
In various implementations, the ingest server 520 may also provide programming identification and time data 514. In various implementations, the programming identification and time data 514 is synchronized with the known media cue data 517, meaning that the programming identification and time data 514 identifies the known media cues 517 and/or provides the times at which the media associated with the known media cues are expected to be displayed. The program identification and time data 514 may also be called metadata.
In various implementations, the known media cue data 517 provide a cue or key for identifying video and/or audio data. The known media cue data 517 may have been taken from known audio-video media, such that the known media cues 517 can be associated with the name and/or some other identification information for the known audio-video media. As described in further detail below, the known media cues 517 can be matched against similar cues taken from media being displayed or played by the interactive television system 501. The matching server 509 may store the known media cue data 517 and the programming identification data 514 in a database 512.
In various implementations, the matching server 509 may include a channel recognition system 510. The channel recognition system 510 may receive unknown media cues 507a from the interactive television system 501. For example, the TV client 503 may take samples from audio-video data being displayed or played at any given time, and may generate cues from the samples. The TV client 503 may provide these cues, as unknown media cues 507a, to the channel recognition system 510 in the matching server 509. The channel recognition system 510 may then match the unknown media cues 507a against known media cues 517 to identify the media being displayed or played by the interactive television system 501.
In various implementations, the channel recognition system 510 determine a program identification for the unknown media cues 507a. The program identification may include a name or description, or some other information that identifies the media content being displayed by the interactive television system 501. The channel recognition system 510 may also provide a time, where the time indicates the time at which the media was played by the interactive television system 501
In various implementations, the channel recognition system 510 may provide the program identification and time data 513 to a contextual targeting manager 511. Using the program identification and time data 513, the contextual manager 511 may determine contextually-related content 507b, including for example applications and advertising. The contextual targeting manager 511 may provide the contextually-related content 507b to the interactive television system 501. For example, the interactive television system 501 may include a contextual targeting engine 502 for managing the contextually-related content 507b. In some implementations, the contextual targeting manager 511 may also provide event triggers 507c to the contextual targeting system 502. The event trigger 507c may instruct the contextual targeting engine 502 to play or display the contextually-related content 507b. For example, an event trigger 507c may instruct the contextual targeting engine 502 to display contextually-related information overlays, where the information overlays are coordinated with the display of video content to which the information overlays are related. Alternatively or additionally, the event triggers 507c may cause substitute media, such as targeted advertising, to be displayed. In some implementations, the contextual targeting engine 502 may provide event confirmation 507d to the contextual targeting manager 511, indicating that the instructions provided by the event trigger 507c has been executed.
In various implementations, the contextual targeting client 502 may alternatively or additionally provide viewership information to the matching server 509. For example, the contextual targeting client 502 may provide viewership information in addition to or instead of event confirmations 507d. In this implementations, the viewership information may include, for example, information about how often a particular media segment was played, what time of day or day of the week the media segment was played, what was played before and/or after the media segment, and/or on what channel the media segment was played. In some cases, the viewership information may also include information about a viewer, such as demographic information.
In various implementations, a media display device may configured with or connected to a video matching system. The video matching system may be able to identify media being displayed or played by the media display device at any given moment in time. As discussed above, the video matching system may take video and/or audio samples for the media being played by the device, generate identifiers or “cues” from the samples, and then match the cues against a database. by identifying the media being displayed or played on a media display device, the video matching system may be able to provide contextually-related content, including applications, advertisements, and/or alternate media content.
When multiple content streams or channels that are available to a media display device play the same content, such as for example “breaking news,” the content may not be uniquely identifiable. For example, without additional information, it may not be clear which channel is being displayed by the media display device.
In this example, channel 2601 plays two segments 602, 604 of regularly scheduled programming. Assuming that a media display device is playing channel 2601, during two time intervals t1603 and t2605, the media display device sends samples from segment 1602 and segment 2604 to the video matching system. By the end of time interval t1603, the video matching system is able to identify segment 1602, and by the end of time interval t2605, the video matching system is able to identify segment 2604.
During a third segment 606, channel 2601 is interrupted by common media segment, here a live pool feed segment 608. The live pool feed segment 608 of this example is common media segment being provided by, for example, a national broadcaster. The live pool feed segment 608 may be made available to each of its syndicated stations. An example of a live pool feed segment is “breaking news,” that is, a national news story. Other example of a live pool feed segments include sporting events, syndicated programs, and commercials. During a time interval t3607, the media display device may send samples from the live pool feed segment 608 to the video matching system.
The video matching system may determine that the samples provided during time interval t3607 were for a live pool feed segment 608. In various implementations, the video matching system may make this determination based on finding matching cues for the live pool feed segment that are associated with multiple channels. Upon determining that channel 2601 is displaying the live pool feed segment 608, in some implementations, the video matching system may treat the live pool segment 608 as a continuation of programming most recently detected on channel 2601. In these implementations, the video matching system may make this determination based on a low probability that the viewer changed the channel at the exact moment that the live pool feed segment 608 started. In some implementations, the video matching system may further determine that the unexpected live pool feed segment 608 is not likely to be related to any scheduled interactive or targeted content. Hence, in these implementations, the scheduled interactive or targeted content may be suppressed or disabled. The targeted content may not be related to the unscheduled live pool feed segment 608, hence displaying, for example, interactive overlays may not be useful to the viewer.
At the conclusion of the live pool feed segment 608, channel 2601 may display segment 4609. In some cases, segment 4609 may be scheduled programming, meaning that the live pool feed segment 608 has interrupted, or come at the end of, segment 3606, and is being played instead of whatever was scheduled to follow segment 3606. For example, segment 4609 may be a continuation of the program of segment 3606, picking up at the point in the program where the program would have been if the live pool segment 608 had not played. As another example, segment 4609 may be a new program that was scheduled to start after segment 3606. Segment 4609 may start at the beginning of the new program, or some part of the beginning may have been overridden by the live pool feed segment.
In some cases, rather than overriding programming that would have been displayed between segment 3606 and segment 4609, the program of segment 3606 may instead by suspended. Once the live pool feed segment 608 ends, the program of segment 3606 may resume in segment 4609, picking up where the program stopped in segment 3. Alternatively, the program of segment 3606 may restart in segment 4609. In some implementations, at the end of the live pool feed segment 608, the viewer may be given the option of resuming the program of segment 3606 or starting the program over from the beginning.
Another example in
At the conclusion of the live pool feed segment 613, channel 7610 reverts to scheduled programmed with segment 3616. During a time interval t3617, the media display device may send samples from segment 3616 to the video matching system, and at this point, the video matching system may be able to determine that the media display device is tuned to channel 7610. In some implementations, the video matching system may associate samples taken during time intervals t1614 and t2615 with channel 7610. The video matching system may further provide contextually-related content related to channel 7610 to the viewer.
A further example is illustrated in
In some situations, a channel may start displaying a common pool feed after the common pool feed segment has started.
Channel 7710 of this example displays regularly scheduled segment 1711 and segment 2712. Though the live pool feed segment 713 starts at point in time 721, channel 7710 delays switching to the live pool feed segment 713. This delay may be because, for example, segment 2712 ran overtime, because the programmers of channel 7710 determined to allow segment 2712 to finish, and/or because segment 2712 included an introduction to the live pool feed segment 713. Assuming a media display device tuned into channel 7710 at around time 721, the media display device may send samples during time intervals t1714 and t2715 to a video matching system. The video matching system may determine that channel 7710 is playing the live pool feed segment 713, but may not be able to determine which channel the media display device is playing.
In another example scenario, a media display device may initially be tuned into channel 71310, and then at time 721 switch to channel 2701. At time 721, channel 2701 may already be playing the live pool feed segment 708. Because the live pool feed segment 708 is associated with multiple channels, during the time intervals t2705 and t3707, the video matching system may not be able to determine which channel the media display device has changed to. Once segment 4709 displays, the video matching device can determine that the media display device is tuned into channel 2701. Upon making this determination, the video matching system may associate samples from t2705 and t3707 with channel 2701.
At around the same time, the two people 808a, 808b maybe tune their display devices 806a, 806b into the same media segment 810. For example, the two people 808a, 808b may individually decide to watch the same movie. As a result, the two media display devices 806a, 806b may be displaying exactly the same media segment 810 at a given moment. Alternatively, at a given moment there may be a time of difference of a few seconds or a few minutes between the content being displayed by each device 806a, 806b. For example, the TV 806a may be a few seconds ahead of the laptop 806b in the movie.
In this example, the media content streams 804a, 804b are digital audio-video streams and/or audio streams being delivered over the Internet 850. For example, the media content streams may include movies, television shows, music, text, and/or images being provided by a website. The media content streams 804a, 804b may each be provided by different content providers, Provider A 802a and Provider B 802b. Providers A 802a and B 802b may be, for example, Internet movie, music, and/or television providers. Alternatively, in some cases, Provider A 802a and Provider B 802b may be the same content provider.
In the example of
As discussed above, when the two example media content streams 804a, 804b are displaying the same media segment 810 at about the same time, the video matching system may not be able to determine some information. For example, while the video matching system may be able to identify the TV 806a and the laptop 806b that are playing the common media segment 810, this information alone may not be enough for the video matching system to determine contextually-related content that is specific to each device. For example, should the video matching system be provided with information such as characteristics or an identity of the individual people 808a, 808b watching the two devices, the video matching system may be able to tailor contextually-related content for the first person 808a while providing different contextually-related content for the second person 808b.
To determine contextually-related content, the video matching system may use the methods discussed above with respect to
As another example, the person 808b watching the laptop 806b may not have been using the laptop 806b to view media content prior to tuning into the common media segment 810. Instead, this person 808b may, either in the middle of the common media segment 810 or after, view other media content. For example, during a commercial break in the common media segment 810, the person 808b may use the laptop 806b to shop for school supplies. The video matching system may use this other media content, displayed in the middle of or after the common media segment 810, to identify the media content stream 804b. For example, the video matching system may identify the media content stream as associated with the laptop 806b and/or the person 808b using the laptop 806b. The video matching system may further provide contextually-related content to the laptop 806b. For example, the video matching system may provide advertising related to school supplies, suggestions for where to shop, and/or suggestions for where to find sales.
In various implementations, a video matching system may use other methods to identify a media content stream when the media content stream is playing a common media segment. In some cases, the provider of the media content stream may provide a graphic element, superimposed onto the common media segment.
In various implementations, a method for detecting a graphic overlay may examine the video display, and find video image edges. Video image edges can be detecting by looking for high contrast differences between sections of a screen of a media display device. The method may further include monitoring whether the detected edges remain stationary. When the detected edges remain in particular locations for longer than a short duration, the video matching systems may determine that it has found an on-screen graphic. For example, the video matching system may look for high-contrast difference across the bottom area of the screen, which may indicate the presence of an on-screen banner.
In various implementations, the video matching system described above can include methods for detecting a graphic overlay. For example, as discussed above, pixel patches can defined for the screen of a media display device. A “pixel patch” may be defined as a block of pixels that are sampled from the screen of a media display device. A pixel patch may contain some number of pixels, each of which can have, for example, RGB color values (or YUV or color values expressed in some other format). For the purposes of graphics overlay detection, the pixel patches may be, for example, 32 pixels wide by 32 pixels high, or a multiple of 32 pixels, such as 64 pixels wide by 64 pixels high. These example sizes may take advantage of the discrete cosine transform (DCT). A discrete cosine transform function can be performed by the video matching system. Edges of a graphic overlay can be detected by examining the coefficients in the lower right quadrant of the discrete cosine transform for each pixel patch
In various implementations, the detection process could also include detecting, over a predetermined length of time, whether high-frequency information from the discrete cosine transform is unchanged. When the high-frequency information does not change, a graphic overlay may be present. In these implementations, some onscreen graphics, such as scrolling banners, can be identified.
In various implementations, other onscreen graphic detection methods can use algorithms such as Sobel and Sharr, or can use an algorithm from the perceptual hashing family of image analysis. As with discrete cosine transform, these algorithms can also be used to detect edges, as well as corners, of graphical elements within video signals. In some cases, a pixel patch with an odd number of pixels, such as 3 pixels by 3 pixels, may be used in a convolution-coded stepwise sweep over a video area of interest, to search for edges.
In various implementations, detecting an onscreen graphic may begin with reducing the pixel information in a pixel patch from an 8-bit Red-Green-Blue (RGB) value an 8-bit monochrome value. Next, a Gaussian blur may be applied to reduce noise in the video information. Next the pixel matrix (that is, the resulting pixel patch) may be passed over the video area of interest. This matrix can then be used to calculate a first-order differential of the pixel values relative to either the vertical or horizontal axis of the video screen. The computed differential is left behind in the respective pixel locations. This differential can be examined for maximum values, which may indicate edges.
In various implementations, another method for detecting graphics overlays is to train the video matching system with various graphics that can be used for graphic overlays. An image matching algorithm can then be used to match the trained or learned graphics to the pixels on the screen. For example, the video matching system can use a perceptual hash (pHash) approach to perform the matching. Examples of other frame comparison methods include the Scale-invariant feature transform (SIFT) and Speeded Up Robust Features (SURF). In implementations where pHash is used, entire video frames can be quickly processed. The resulting hash values may be compared to reference video images. These reference video images may also processed using pHash, and may be supplied from a central server. One of the advantages of using pHash is that it may be able to reliably match coarse features (e.g. large rectangles or other shapes that may be used by graphic overlays) with relatively high insensitivity to contrast, brightness, or color changes. Another advantage of pHash is its ability to also match detailed individual video frames.
In various implementations, the video matching system may further maintain a library of different possible graphics overlay comparison candidates. Furthermore, the video matching system may use the library without increasing the total number of total image searches that are conducted within a unit of time. Specifically, in some implementations, the video matching system may track successful detections. Graphics overlay comparison candidates that match successfully and frequently may be more likely to match in the future, while candidates that match infrequently or that have not successfully matched are less likely to match in the future.
In various implementations, graphics overlay detection can be interleaved with processes for automated content recognition.
At step 1002, the computing device may receive a plurality of media content streams. The computing device may be configured to identify media content being played by a particular media display device (e.g., a television, a tablet computer, a laptop, etc.) at a particular time. At least two of the plurality of media content streams may concurrently include a same unscheduled segment. For example, two media content streams may both include a “breaking news” segment, that is, a national broadcast of a significant event. As another example, to media content streams may both include the same streaming movie, where the movie was requested by different people using different media display devices. In this example, the media segment is “unscheduled” because there may not be a schedule of programming associated with the media display devices.
At step 1004, the computing device may determine that the particular media display device is playing the unscheduled media segment in a media content stream at a current time. The computing device may make this determining by examining the media content available at the current time in each of the plurality of media content streams. For example, the plurality of media content streams may include two or more local televisions channels, and two or more of these local television channels may both be receiving a breaking news feed.
At step 1006, the computing device may determine identification information from the media content included in the media content stream being played by the particular media display device at the current time. For example, the computing device may use identification information provided by media content that was played by the particular media display device before the unscheduled media segment. Alternatively or additionally, the computing device may use identification information provided by media content that is played after the unscheduled media segment. The identification information may identify the media content stream. For example, the identification information may identify a channel, a service provider, the particular media display device, and/or the person using the particular media display device.
At step 1008, the computing device may determine contextually-related content. Contextually-related content may include, for example, interactive information, advertisements, and/or suggestions for additional content, among other things. The contextually-related content may be disabled while the unscheduled media segment is being played by the particular media display device.
At step 1010, the computing device may display the media content stream and the contextually-related content after the unscheduled media segment has been played. For example, the computing device may overlay the contextually-related information over media content that follows the unscheduled media segment. Alternatively or additionally, the computing device may insert the contextually-related information after the unscheduled media segment and before additional media content is played.
Various methods related to matching cues from unknown media content to candidates in a reference database will now be discussed in greater detail. These methods include the nearest neighbor search process discussed above with respect to
As discussed above, a video matching system can be configured to identify a media content stream when the media content stream includes an unscheduled media segment. As further discussed above, identifying the media content stream may include identifying media content played by a media display device before or after the unscheduled media segment. Processes for identifying media content are discussed above with respect to
The video matching system may further include various methods to improve the efficiency of finding matches in the database. The database may contain an enormous number of cues, and thus the video matching system may include algorithms for finding potential matches, or “candidates” to match against. The video matching system may further include algorithms to determine which candidate cues actually match cues generated from the media content device's display mechanism. Locating candidate cues may be more efficient than other methods for matching cue values against the values in the database, such as matching a cue against every entry in the database.
Nearest neighbor and path pursuit are examples of techniques that can be used to match unknown cues to candidate cues in the reference database. Path pursuit is a mathematical method for identifying a related sequence of points from among many possible points. Nearest neighbor is a method that can be used to identify candidate points for conducting a path pursuit. Below, an example applying path nearest neighbor and path pursuit to tracking video transmission using ambiguous cues is given, but the general concept can be applied to any field where match candidates are to be selected from a reference database.
A method for efficient video pursuit is presented. Video pursuit is the application path pursuit techniques to the problem of locating matching candidates in a video reference database given unknown video cues. Given a large number of video segments, the system must be able to identify in real time what segment a given query video input is taken from and in what time offset. The segment and offset together are referred to as the location. The method is called video pursuit since it must be able to efficiently detect and adapt to pausing, fast forwarding, rewinding, abrupt switching to other segments, and switching to unknown segments. Before being able to pursue live video the database is processed. Visual cues (a handful of pixel values) are taken from frames every constant fraction of a second and put in specialized data structure (note that this can also be done in real time). The video pursuit is performed by continuously receiving cues from the input video and updating a set of beliefs or estimates about its current location. Each cue either agrees or disagrees with the estimates, and they are adjusted to reflect the new evidence. A video location is assumed to be the correct one if the confidence in this being true is high enough. By tracking only a small set of possible “suspect” locations, this can be done efficiently.
A method is described for video pursuit but uses mathematical constructs to explain and investigate it. It is the aim of this introduction to give the reader the necessary tools to translate between the two domains. A video signal is comprised of sequential frames. Each can be thought of as a still image. Every frame is a raster of pixels. Each pixel is made out of three intensity values corresponding to the red, green, and blue (RGB) make of that pixel's color. In the terminology used herein, a cue is a list of RGB values of a subset of the pixels in a frame and a corresponding time stamp. The number of pixels in a cue is significantly smaller than in a frame, usually between 5 and 15. Being an ordered list of scalar values, the cue values are in fact a vector. This vector is also referred to as a point.
Although these points are in high dimension, usually between 15 and 150, they can be imagined as points in two dimensions. In fact, the illustrations will be given as two dimensional plots. Now, consider the progression of a video and its corresponding cue points. Usually a small change in time produces a small change in pixel values. The pixel point can be viewed as “moving” a little between frames. Following these tiny movements from frame to frame, the cue follows a path in space like a bead would on a bent wire.
In the language of this analogy, in video pursuit the locations of the bead in space (the cue points) are received and the part of wire (path) the bead is following is looked for. This is made significantly harder by two facts. First, the bead does not follow the wire exactly but rather keeps some varying unknown distance from it. Second, the wires are all tangled together. These statements are made exact in section 2. The algorithm described below does this in two conceptual steps. When a cue is received, the algorithm looks for all points on all the known paths that are sufficiently close to the cue point; these are called suspects. This is done efficiently using the Probabilistic Point Location in Equal Balls algorithm. These suspects are added to a history data structure and the probability of each of them indicating the true location is calculated. This step also includes removing suspect locations that are sufficiently unlikely. This history update process ensures that on the one hand only a small history is kept but on the other hand no probable locations are ever deleted. The generic algorithm is given in Algorithm 1 and illustrated in
The following sections begin with describing the Probabilistic Point Location in Equal Balls (PPLEB) algorithm in Section 1. the PPLEB algorithm is used in order to perform line 5 in Algorithm 1 above efficiently. The ability to perform this search for suspects quickly is crucial for the applicability of this method. In section 2 one possible statistical model is described for performing lines 6 and 7. The described model is a natural choice for the setup. It is also shown how it can be used very efficiently.
Section 1—Probabilistic Point Location in Equal Balls
The following section describes a simple algorithm for performing probabilistic point location in equal balls (PPLEB). In the traditional PLEB (point location in equal balls), one starts with a set of n points x, in JR d and a specified ball of radius r. The algorithm is given O(poly(n)) preprocessing time to produce an efficient data structure. Then, given a query point x the algorithm is required to return all points x, such that ∥x−xi∥≤r. The set of points such that ∥x−xi∥≤r geometrically lie within a ball of radius r surrounding the query x (see
The problem of PPLEB and the problem of nearest neighbor search are two similar problems that received much attention in the academic community. In fact, these problems were among the first studied in the field of computational geometry. Many different methods cater to the case where the ambient dimension is small or constant. These partition the space in different ways and recursively search through the parts. These methods include KD-trees, cover-trees, and others. Although very efficient in low dimension, when the ambient dimension is high, they tend to perform very poorly. This is known as the “curse of dimensionality”. Various approaches attempt to solve this problem while overcoming the curse of dimensionality. The algorithm used herein uses a simpler and faster version of the algorithm and can rely on Local Sensitive Hashing.
Section 1.1 Locality Sensitive Hashing
In the scheme of local sensitive hashing, one devises a family of hash functions H such that:
In words, the probability of x and y being mapped to the same value by h is significantly higher if they are close to each other.
For the sake of clarity, let us first deal with a simplified scenario where all incoming vectors are of the same length r′ and r′>√{square root over (2r)}. The reason for the latter condition will become clear later. First a random function u c U is defined, which separates between x and y according to the angle between them. Let {right arrow over (u)} be a random vector chosen uniformly from the unit sphere Sd-1 and let u(x)=sign ({right arrow over (u)}·x) (See
The family of functions H is set to be a cross product of t independent copies of u, i.e. h(x)=[u1(x), . . . , u1(x)]. Intuitively, one would like to have that if h(x)=h(y) then x and y are likely to be close to each other. Let us quantify that. First, compute the expected number of false positive mistakes nfp. These are the cases for which h(x)=h(y) but ∥x−y∥>2r. A value t is found for which nfp is no more than 1, i.e. one is not expected to be wrong.
Now, the probability that h(x)=h(y) given that they are neighbors is computed:
Note here that one must have that 2p<1 which requires r′>√{square root over (2r)}. This might not sound like a very high success probability. Indeed, 1/√{square root over (n)} is significantly smaller than ½. The next section will describe how to boost this probability up to ½.
Section 1.2 the Point Search Algorithm
function h maps every point in space to a bucket. Define the bucket function Bh: d→2[n] of a point x with respect to hash function h as Bh(x)≡{xi|h(xi)=h(x)}. The data structure maintained is m=O(√{square root over (n)}) instances of bucket functions [Bh1, . . . , Bhm]. When one searches for a point x, the function returns B(x)=∪iBh
In other words, while with probability at least % A each neighbor of x is found, one is not likely to find many non-neighbors.
Section 1.3 Dealing with Different Radii Input Vectors
The previous sections only dealt with searching through vectors of the same length, namely r′. Now described is how one can use the construction as a building block to support a search in different radii. As seen in
Section 2 The Path Pursuit Problem
In the path pursuit problem, a fixed path in space is given along with the positions of a particle in a sequence of time points. The terms particle, cue, and point will be used interchangeably. The algorithm is required to output the position of the particle on the path. This is made harder by a few factors: The particle only follows the path approximately; the path can be discontinuous and intersect itself many times; both particle and path positions are given in a sequence of time points (different for each).
It is important to note that this problem can simulate tracking a particle on any number of paths. This is simply done by concatenating the paths into one long path and interpreting the resulting position as the position on the individual paths.
More precisely, let path P be parametric curve P: →
d. The curve parameter will be referred to as the time. The points on the path that are known to us are given in arbitrary time points ti, i.e. n pairs (ti, P(ti)) are given. The particle follows the path but its positions are given in different time points, as shown in
Section 2.1 Likelihood Estimation
Since the particle does not follow the path exactly and since the path can intersect itself many times it is usually impossible to positively identify the position on the path the particle is actually on. Therefore, a probability distribution is computed on all possible path locations. If a location probability is significantly probable, the particle position is assumed to be known. The following section describes how this can be done efficiently.
If the particle is following the path, then the time difference between the particle time stamp and the offset of the corresponding points on P should be relatively fixed. In other words, if x(t′) is currently in offset t on the path then it should be close to P(t). Also, r seconds ago it should have been in offset t−τ. Thus x(t′−τ) should be close to P(t−τ) (note that if the particle is intersecting the path, and x(t′) is close to P(t) temporarily, it is unlikely that x(t′−τ) and P(t−τ) will also be close). Define the relative offset as Δ=t−t′. Notice that as long as the particle is following the path the relative offset Δ remains unchanged. Namely, x(t′) is close to P(t′+Δ).
The maximum likelihood relative offset is obtained by calculating:
In words, the most likely relative offset is the one for which the history of the particle is most likely. This equation however cannot be solved without a statistical model. This model must quantify: how tightly x follows the path; how likely it is that x jumps between locations; and how smooth the path and particle curves are between the measured points.
Section 2.2 Time Discounted Binning
Now described is a statistical model for estimating the likelihood function. The model makes the assumption that the particle's deviation away from the path distributes normally with standard deviation ar. It also assumes that at any given point in time, there is some non-zero probability the particle will abruptly switch to another path. This is manifested by an exponential discount with time for past points. Apart for being a reasonable choice for a modeling point of view this model also has the advantage of being efficiently updateable. For some constant time unit 1, set the likelihood function to be proportional to f which is defined as follows:
Here α<<1 is a scale coefficient and is the probability that the particle will jump to a random location on the path in a given time unit.
Updating the function f efficiently can be achieved using the following simple observation.
Moreover, since α<<1, if ∥x(t′m)−P(ti)∥≥r, the follow occurs:
This is an important property of the likelihood function since the sum update can now performed only over the neighbors of x(t′1) and not the entire path. Denote by S the set of (ti, P(ti)) such that ∥x(t′m)−P(ti)∥≤r. The follow equation occurs:
This is described in Algorithm 2.2 below. The term f is used as a sparse vector that receives also negative integer indices. The set S is the set of all neighbors of x(ti) on the path and can be computed quickly using the PPLEB algorithm. It is easy to verify that if the number of neighbors of x(ti) is bounded by some constant nnear then the number of non-zeros in the vector f is bounded by nnear/(which is only a constant factor larger. The final stage of the algorithm is to output a specific value of δ if f(└δ/τ┘) is above some threshold value.
In
In the preceding description, for the purposes of explanation, specific details are set forth in order to provide a thorough understanding of various examples. However, it will be apparent that various examples may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the examples in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the examples. The figures and description are not intended to be restrictive.
The preceding description provides exemplary illustrations only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the preceding description of the examples will provide those skilled in the art with an enabling description for implementing the various examples. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the invention as set forth in the appended claims.
Also, it is noted that individual examples may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed, but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.
The term “machine-readable storage medium” or “computer-readable storage medium” includes, but is not limited to, portable or non-portable storage devices, optical storage devices, and various other mediums capable of storing, containing, or carrying instruction(s) and/or data. A machine-readable storage medium or computer-readable storage medium may include a non-transitory medium in which data can be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, or other information may be passed, forwarded, or transmitted using any suitable means including memory sharing, message passing, token passing, network transmission, or other transmission technique.
Furthermore, examples may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a machine-readable medium. A processor(s) may perform the necessary tasks.
Systems depicted in some of the figures may be provided in various configurations. In some examples, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system.
As described in further detail above, certain aspects and features of the present disclosure relate to identifying unknown data points by comparing the unknown data points to one or more reference data points. The systems and methods described herein improve the efficiency of storing and searching large datasets that are used to identify the unknown data points. For example, the systems and methods allow identification of the unknown data points while reducing the density of the large dataset required to perform the identification. The techniques can be applied to any system that harvests and manipulates large volumes of data. Illustrative examples of these systems include automated content-based searching systems (e.g., automated content recognition for video-related applications or other suitable application), MapReduce systems, Bigtable systems, pattern recognition systems, facial recognition systems, classification systems, computer vision systems, data compression systems, cluster analysis, or any other suitable system. One of ordinary skill in the art will appreciate that the techniques described herein can be applied to any other system that stores data that is compared to unknown data. In the context of automated content recognition (ACR), for example, the systems and methods reduce the amount of data that must be stored in order for a matching system to search and find relationships between unknown and known data groups.
By way of example only and without limitation, some examples described herein use an automated audio and/or video content recognition system for illustrative purposes. However, one of ordinary skill in the art will appreciate that the other systems can use the same techniques.
Substantial variations may be made in accordance with specific requirements. For example, customized hardware might also be used, and/or particular elements might be implemented in hardware, software (including portable software, such as applets, etc.), or both. Further, connection to other access or computing devices such as network input/output devices may be employed.
In the foregoing specification, aspects of the various implementations are described with reference to specific examples thereof, but those skilled in the art will recognize that the implementations is not limited thereto. Various features and aspects of the above-described implementations may be used individually or jointly. Further, examples can be utilized in any number of environments and applications beyond those described herein without departing from the broader spirit and scope of the specification. The specification and drawings are, accordingly, to be regarded as illustrative rather than restrictive.
In the foregoing description, for the purposes of illustration, methods were described in a particular order. It should be appreciated that in alternate examples, the methods may be performed in a different order than that described. It should also be appreciated that the methods described above may be performed by hardware components or may be embodied in sequences of machine-executable instructions, which may be used to cause a machine, such as a general-purpose or special-purpose processor or logic circuits programmed with the instructions to perform the methods. These machine-executable instructions may be stored on one or more machine readable mediums, such as CD-ROMs or other type of optical disks, floppy diskettes, ROMs, RAMs, EPROMs, EEPROMs, magnetic or optical cards, flash memory, or other types of machine-readable mediums suitable for storing electronic instructions. Alternatively, the methods may be performed by a combination of hardware and software.
Where components are described as being configured to perform certain operations, such configuration can be accomplished, for example, by designing electronic circuits or other hardware to perform the operation, by programming programmable electronic circuits (e.g., microprocessors, or other suitable electronic circuits) to perform the operation, or any combination thereof.
While illustrative examples of the application have been described in detail herein, it is to be understood that the inventive concepts may be otherwise variously embodied and employed, and that the appended claims are intended to be construed to include such variations, except as limited by the prior art.
This application is a continuation of U.S. patent application Ser. No. 16/444,152, filed Jun. 18, 2019, which is a continuation of U.S. patent application Ser. No. 15/211,345, filed Jul. 15, 2016, which claims the benefit of U.S. Provisional Application No. 62/193,322, filed on Jul. 16, 2015, the disclosure of each of which is incorporated by reference herein in its entirety for all purposes. This application is related to U.S. patent application Ser. No. 14/089,003 filed on Nov. 25, 2013, now U.S. Pat. No. 8,898,714, issued on Nov. 25, 2014; U.S. patent application Ser. No. 14/217,075, now U.S. Pat. No. 9,055,309, issued on Jun. 9, 2015; U.S. Provisional Application No. 61/182,334 filed on May 29, 2009; U.S. Provisional Application No. 61/290,714 filed on Dec. 29, 2009; U.S. patent application Ser. No. 12/788,748, now U.S. Pat. No. 8,769,584, issued on Jul. 1, 2014; and U.S. patent application Ser. No. 12/788,721, now U.S. Patent No. 595,781, issued on Nov. 26, 2013, all of which are hereby incorporated by reference in their entirety.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4677466 | Lert, Jr. et al. | Jun 1987 | A |
| 4697209 | Kiewit et al. | Sep 1987 | A |
| 4739398 | Thomas et al. | Apr 1988 | A |
| 5019899 | Boles et al. | May 1991 | A |
| 5193001 | Kerdranvrat | Mar 1993 | A |
| 5210820 | Kenyon | May 1993 | A |
| 5319453 | Copriviza et al. | Jun 1994 | A |
| 5436653 | Ellis et al. | Jul 1995 | A |
| 5473607 | Hausman | Dec 1995 | A |
| 5481294 | Thomas et al. | Jan 1996 | A |
| 5557334 | Legate | Sep 1996 | A |
| 5572246 | Ellis et al. | Nov 1996 | A |
| 5650828 | Lee | Jul 1997 | A |
| 5812286 | Li | Sep 1998 | A |
| 5826165 | Echeita et al. | Oct 1998 | A |
| 5918223 | Blum et al. | Jun 1999 | A |
| 6008802 | Goldschmidt et al. | Dec 1999 | A |
| 6025837 | Matthews, III et al. | Feb 2000 | A |
| 6035177 | Moses et al. | Mar 2000 | A |
| 6064764 | Bhaskaran et al. | May 2000 | A |
| 6298482 | Seidman | Oct 2001 | B1 |
| 6381362 | Deshpande et al. | Apr 2002 | B1 |
| 6415438 | Blackketter et al. | Jul 2002 | B1 |
| 6463585 | Hendricks et al. | Oct 2002 | B1 |
| 6469749 | Dimitrova | Oct 2002 | B1 |
| 6577346 | Perlman | Jun 2003 | B1 |
| 6577405 | Kranz et al. | Jun 2003 | B2 |
| 6628801 | Powell et al. | Sep 2003 | B2 |
| 6647548 | Lu et al. | Nov 2003 | B1 |
| 6675174 | Bolle et al. | Jan 2004 | B1 |
| 6771316 | Iggulden | Aug 2004 | B1 |
| 6804659 | Graham et al. | Oct 2004 | B1 |
| 6978470 | Swix et al. | Dec 2005 | B2 |
| 6990453 | Wang et al. | Jan 2006 | B2 |
| 7028327 | Dougherty et al. | Apr 2006 | B1 |
| 7039930 | Goodman et al. | May 2006 | B1 |
| 7050068 | Bastos et al. | May 2006 | B1 |
| 7051351 | Goldman et al. | May 2006 | B2 |
| 7064796 | Roy et al. | Jun 2006 | B2 |
| 7089575 | Agnihotri et al. | Aug 2006 | B2 |
| 7098959 | Mishima et al. | Aug 2006 | B2 |
| 7136875 | Anderson et al. | Nov 2006 | B2 |
| 7178106 | Lamkin et al. | Feb 2007 | B2 |
| 7210157 | Devara | Apr 2007 | B2 |
| 7243239 | Kirovski | Jul 2007 | B2 |
| 7325013 | Camso | Jan 2008 | B2 |
| 7346512 | Wang et al. | Mar 2008 | B2 |
| 7356830 | Dimitrova | Apr 2008 | B1 |
| 7421723 | Harkness et al. | Sep 2008 | B2 |
| 7545984 | Kiel et al. | Jun 2009 | B1 |
| 7590998 | Hanley | Sep 2009 | B2 |
| 7623823 | Zito et al. | Nov 2009 | B2 |
| 7787696 | Wilhelm et al. | Aug 2010 | B2 |
| 7793318 | Deng | Sep 2010 | B2 |
| 7933451 | Kloer | Apr 2011 | B2 |
| 7991764 | Rathod | Aug 2011 | B2 |
| 8001571 | Schwartz et al. | Aug 2011 | B1 |
| 8094872 | Yagnik et al. | Jan 2012 | B1 |
| 8171004 | Kaminski, Jr. et al. | May 2012 | B1 |
| 8171030 | Peira et al. | May 2012 | B2 |
| 8175413 | Ioffe et al. | May 2012 | B1 |
| 8189945 | Stojancic et al. | May 2012 | B2 |
| 8195589 | Bakke et al. | Jun 2012 | B2 |
| 8195689 | Ramanathan et al. | Jun 2012 | B2 |
| 8229227 | Stojancic et al. | Jul 2012 | B2 |
| 8335786 | Peira et al. | Dec 2012 | B2 |
| 8352535 | Peled | Jan 2013 | B2 |
| 8364703 | Ramanathan et al. | Jan 2013 | B2 |
| 8385644 | Stojancic et al. | Feb 2013 | B2 |
| 8392789 | Biscondi et al. | Mar 2013 | B2 |
| 8477232 | Forutanpour | Jul 2013 | B2 |
| 8494234 | Djordjevic et al. | Jul 2013 | B1 |
| 8522283 | Laligand et al. | Aug 2013 | B2 |
| 8595781 | Neumeier et al. | Nov 2013 | B2 |
| 8619877 | McDowell | Dec 2013 | B2 |
| 8625902 | Baheti et al. | Jan 2014 | B2 |
| 8769854 | Battaglia | Jul 2014 | B1 |
| 8776105 | Sinha et al. | Jul 2014 | B2 |
| 8788328 | George | Jul 2014 | B1 |
| 8832723 | Sinha et al. | Sep 2014 | B2 |
| 8856817 | Sinha et al. | Oct 2014 | B2 |
| 8893167 | Sinha et al. | Nov 2014 | B2 |
| 8893168 | Sinha et al. | Nov 2014 | B2 |
| 8898714 | Neumeier et al. | Nov 2014 | B2 |
| 8918804 | Sinha et al. | Dec 2014 | B2 |
| 8918832 | Sinha et al. | Dec 2014 | B2 |
| 8930980 | Neumeier et al. | Jan 2015 | B2 |
| 8953891 | Hundemer | Feb 2015 | B1 |
| 8959202 | Haitsma et al. | Feb 2015 | B2 |
| 9055309 | Neumeier et al. | Jun 2015 | B2 |
| 9055335 | Neumeier et al. | Jun 2015 | B2 |
| 9071868 | Neumeier et al. | Jun 2015 | B2 |
| 9083710 | Yadav | Jul 2015 | B1 |
| 9094714 | Neumeier et al. | Jul 2015 | B2 |
| 9094715 | Neumeier et al. | Jul 2015 | B2 |
| 9262671 | Unzueta | Feb 2016 | B2 |
| 9368021 | Touloumtzis | Jun 2016 | B2 |
| 9432616 | Hurd | Aug 2016 | B1 |
| 9449090 | Neumeier et al. | Sep 2016 | B2 |
| 9465867 | Hoarty | Oct 2016 | B2 |
| 9484065 | McDowell | Nov 2016 | B2 |
| 9756283 | Hundemer | Sep 2017 | B1 |
| 9838753 | Neumeier et al. | Dec 2017 | B2 |
| 9955192 | Neumeier et al. | Apr 2018 | B2 |
| 10080062 | Neumeier et al. | Sep 2018 | B2 |
| 10116972 | Neumeier et al. | Oct 2018 | B2 |
| 10169455 | Neumeier et al. | Jan 2019 | B2 |
| 10185768 | Neumeier et al. | Jan 2019 | B2 |
| 10192138 | Neumeier et al. | Jan 2019 | B2 |
| 10271098 | Neumeier et al. | Apr 2019 | B2 |
| 10284884 | Neumeier et al. | May 2019 | B2 |
| 10306274 | Neumeier et al. | May 2019 | B2 |
| 10375451 | Neumeier et al. | Aug 2019 | B2 |
| 10405014 | Neumeier et al. | Sep 2019 | B2 |
| 10482349 | Neumeier | Nov 2019 | B2 |
| 10674223 | Neumeier et al. | Jun 2020 | B2 |
| 10820048 | Neumeier et al. | Oct 2020 | B2 |
| 20010039658 | Walton | Nov 2001 | A1 |
| 20010044992 | Jahrling | Nov 2001 | A1 |
| 20020026635 | Wheeler et al. | Feb 2002 | A1 |
| 20020054069 | Britt et al. | May 2002 | A1 |
| 20020054695 | Bjorn et al. | May 2002 | A1 |
| 20020056088 | Silva, Jr. et al. | May 2002 | A1 |
| 20020059633 | Harkness et al. | May 2002 | A1 |
| 20020078144 | Lamkin et al. | Jun 2002 | A1 |
| 20020083060 | Wang et al. | Jun 2002 | A1 |
| 20020083442 | Eldering | Jun 2002 | A1 |
| 20020100041 | Rosenberg et al. | Jul 2002 | A1 |
| 20020105907 | Bruekers et al. | Aug 2002 | A1 |
| 20020120925 | Logan | Aug 2002 | A1 |
| 20020122042 | Bates | Sep 2002 | A1 |
| 20020144262 | Plotnick | Oct 2002 | A1 |
| 20020162117 | Pearson et al. | Oct 2002 | A1 |
| 20020162118 | Levy et al. | Oct 2002 | A1 |
| 20030026422 | Gerheim et al. | Feb 2003 | A1 |
| 20030086341 | Wells | May 2003 | A1 |
| 20030105794 | Jasinschi | Jun 2003 | A1 |
| 20030118243 | Sezer | Jun 2003 | A1 |
| 20030121037 | Swix et al. | Jun 2003 | A1 |
| 20030121046 | Roy et al. | Jun 2003 | A1 |
| 20030147561 | Faibish | Aug 2003 | A1 |
| 20030182291 | Kurupati | Sep 2003 | A1 |
| 20030188321 | Shoff et al. | Oct 2003 | A1 |
| 20030208763 | McElhatten et al. | Nov 2003 | A1 |
| 20040045020 | Witt et al. | Mar 2004 | A1 |
| 20040059708 | Dean et al. | Mar 2004 | A1 |
| 20040183825 | Stauder et al. | Sep 2004 | A1 |
| 20040216171 | Barone et al. | Oct 2004 | A1 |
| 20040221237 | Foote et al. | Nov 2004 | A1 |
| 20040226035 | Hauser | Nov 2004 | A1 |
| 20040237102 | Konig | Nov 2004 | A1 |
| 20040240562 | Bargeron et al. | Dec 2004 | A1 |
| 20050015795 | Iggulden | Jan 2005 | A1 |
| 20050015796 | Bruckner et al. | Jan 2005 | A1 |
| 20050027766 | Ben | Feb 2005 | A1 |
| 20050066352 | Herley | Mar 2005 | A1 |
| 20050120372 | Itakura | Jun 2005 | A1 |
| 20050149968 | Konig et al. | Jul 2005 | A1 |
| 20050172312 | Lienhart et al. | Aug 2005 | A1 |
| 20050207416 | Rajkotia | Sep 2005 | A1 |
| 20050209065 | Schlosser et al. | Sep 2005 | A1 |
| 20050235318 | Grauch et al. | Oct 2005 | A1 |
| 20060029286 | Lim et al. | Feb 2006 | A1 |
| 20060029368 | Harville | Feb 2006 | A1 |
| 20060031914 | Dakss et al. | Feb 2006 | A1 |
| 20060116989 | Bellannkonda | Jun 2006 | A1 |
| 20060120624 | Jojic | Jun 2006 | A1 |
| 20060133647 | Werner et al. | Jun 2006 | A1 |
| 20060153296 | Deng | Jul 2006 | A1 |
| 20060155952 | Haas | Jul 2006 | A1 |
| 20060173831 | Basso et al. | Aug 2006 | A1 |
| 20060187358 | Lienhart et al. | Aug 2006 | A1 |
| 20060193506 | Dorphan et al. | Aug 2006 | A1 |
| 20060195857 | Wheeler et al. | Aug 2006 | A1 |
| 20060195860 | Eldering et al. | Aug 2006 | A1 |
| 20060245724 | Hwang et al. | Nov 2006 | A1 |
| 20060245725 | Lim | Nov 2006 | A1 |
| 20060253330 | Maggio et al. | Nov 2006 | A1 |
| 20060277047 | DeBusk et al. | Dec 2006 | A1 |
| 20060294561 | Grannan et al. | Dec 2006 | A1 |
| 20070009235 | Walters et al. | Jan 2007 | A1 |
| 20070033608 | Eigeldinger | Feb 2007 | A1 |
| 20070050832 | Wright et al. | Mar 2007 | A1 |
| 20070061724 | Slothouber et al. | Mar 2007 | A1 |
| 20070061831 | Savoor et al. | Mar 2007 | A1 |
| 20070083901 | Bond | Apr 2007 | A1 |
| 20070094696 | Sakai | Apr 2007 | A1 |
| 20070109449 | Cheung | May 2007 | A1 |
| 20070113263 | Chatani | May 2007 | A1 |
| 20070124762 | Chickering et al. | May 2007 | A1 |
| 20070139563 | Zhong | Jun 2007 | A1 |
| 20070143796 | Malik | Jun 2007 | A1 |
| 20070168409 | Cheung | Jul 2007 | A1 |
| 20070180459 | Smithpeters et al. | Aug 2007 | A1 |
| 20070192782 | Ramaswamy | Aug 2007 | A1 |
| 20070242880 | Stebbings | Oct 2007 | A1 |
| 20070250901 | McIntire et al. | Oct 2007 | A1 |
| 20070261070 | Brown et al. | Nov 2007 | A1 |
| 20070261075 | Glasberg | Nov 2007 | A1 |
| 20070271300 | Ramaswamy | Nov 2007 | A1 |
| 20070274537 | Srinivasan | Nov 2007 | A1 |
| 20070300280 | Turner et al. | Dec 2007 | A1 |
| 20080005086 | Moore | Jan 2008 | A1 |
| 20080044102 | Ekin | Feb 2008 | A1 |
| 20080046945 | Hanley | Feb 2008 | A1 |
| 20080089551 | Heather et al. | Apr 2008 | A1 |
| 20080138030 | Bryan et al. | Jun 2008 | A1 |
| 20080155588 | Roberts et al. | Jun 2008 | A1 |
| 20080155627 | O'Connor et al. | Jun 2008 | A1 |
| 20080172690 | Kanojia et al. | Jul 2008 | A1 |
| 20080208891 | Wang et al. | Aug 2008 | A1 |
| 20080240562 | Fukuda et al. | Oct 2008 | A1 |
| 20080249986 | Clarke-Martin | Oct 2008 | A1 |
| 20080263620 | Berkvens et al. | Oct 2008 | A1 |
| 20080276266 | Huchital et al. | Nov 2008 | A1 |
| 20080309819 | Hardacker et al. | Dec 2008 | A1 |
| 20080310731 | Stojancic et al. | Dec 2008 | A1 |
| 20080313140 | Pereira et al. | Dec 2008 | A1 |
| 20090007195 | Beyabani | Jan 2009 | A1 |
| 20090024923 | Hartwig et al. | Jan 2009 | A1 |
| 20090028517 | Shen et al. | Jan 2009 | A1 |
| 20090052784 | Covell et al. | Feb 2009 | A1 |
| 20090087027 | Eaton et al. | Apr 2009 | A1 |
| 20090088878 | Otsuka et al. | Apr 2009 | A1 |
| 20090100361 | Abello et al. | Apr 2009 | A1 |
| 20090116702 | Conradt | May 2009 | A1 |
| 20090131861 | Braig et al. | May 2009 | A1 |
| 20090172728 | Shkedi et al. | Jul 2009 | A1 |
| 20090172746 | Aldrey et al. | Jul 2009 | A1 |
| 20090175538 | Bronstein et al. | Jul 2009 | A1 |
| 20090213270 | Ismert et al. | Aug 2009 | A1 |
| 20090235312 | Morad et al. | Sep 2009 | A1 |
| 20100010648 | Bull et al. | Jan 2010 | A1 |
| 20100042790 | Mondal et al. | Feb 2010 | A1 |
| 20100083299 | Nelson | Apr 2010 | A1 |
| 20100115543 | Falcon | May 2010 | A1 |
| 20100115574 | Hardt et al. | May 2010 | A1 |
| 20100125870 | Ukawa et al. | May 2010 | A1 |
| 20100166257 | Wredenhagen | Jul 2010 | A1 |
| 20100199295 | Katpelly et al. | Aug 2010 | A1 |
| 20100202700 | Rezazadeh | Aug 2010 | A1 |
| 20100235486 | White et al. | Sep 2010 | A1 |
| 20100253838 | Garg et al. | Oct 2010 | A1 |
| 20100269138 | Krikorian et al. | Oct 2010 | A1 |
| 20100306805 | Neumeier et al. | Dec 2010 | A1 |
| 20100306808 | Neumeier et al. | Dec 2010 | A1 |
| 20100328529 | Hirayama | Dec 2010 | A1 |
| 20110015996 | Kassoway et al. | Jan 2011 | A1 |
| 20110026761 | Radhakrishnan et al. | Feb 2011 | A1 |
| 20110041154 | Olson | Feb 2011 | A1 |
| 20110055552 | Francis et al. | Mar 2011 | A1 |
| 20110096955 | Voloshynovskiy et al. | Apr 2011 | A1 |
| 20110251987 | Buchheit | Apr 2011 | A1 |
| 20110247042 | Mallinson | Oct 2011 | A1 |
| 20110289099 | Quan | Nov 2011 | A1 |
| 20110299770 | Vaddadi et al. | Dec 2011 | A1 |
| 20110307683 | Spackman | Dec 2011 | A1 |
| 20120017240 | Shkedi | Jan 2012 | A1 |
| 20120054143 | Doig et al. | Mar 2012 | A1 |
| 20120076357 | Yamamoto et al. | Mar 2012 | A1 |
| 20120095958 | Pereira et al. | Apr 2012 | A1 |
| 20120117584 | Gordon | May 2012 | A1 |
| 20120158511 | Lucero et al. | Jun 2012 | A1 |
| 20120174155 | Mowrey et al. | Jul 2012 | A1 |
| 20120177249 | Levy et al. | Jul 2012 | A1 |
| 20120185566 | Nagasaka | Jul 2012 | A1 |
| 20120224749 | Chen | Sep 2012 | A1 |
| 20120272259 | Cortes | Oct 2012 | A1 |
| 20120281924 | Coulombe | Nov 2012 | A1 |
| 20120294586 | Weaver et al. | Nov 2012 | A1 |
| 20120317240 | Wang | Dec 2012 | A1 |
| 20120324494 | Burger et al. | Dec 2012 | A1 |
| 20120327450 | Sagan | Dec 2012 | A1 |
| 20130007191 | Klappert et al. | Jan 2013 | A1 |
| 20130007792 | Jeon et al. | Jan 2013 | A1 |
| 20130042262 | Riethmueller | Feb 2013 | A1 |
| 20130050564 | Adams et al. | Feb 2013 | A1 |
| 20130054356 | Richman et al. | Feb 2013 | A1 |
| 20130058522 | Raesig | Mar 2013 | A1 |
| 20130067523 | Etsuko et al. | Mar 2013 | A1 |
| 20130070847 | Iwamoto et al. | Mar 2013 | A1 |
| 20130108173 | Lienhart et al. | May 2013 | A1 |
| 20130139209 | Urrabazo et al. | May 2013 | A1 |
| 20130198768 | Kitazato | Aug 2013 | A1 |
| 20130202150 | Sinha et al. | Aug 2013 | A1 |
| 20130205315 | Sinha | Aug 2013 | A1 |
| 20130209065 | Yeung | Aug 2013 | A1 |
| 20130212609 | Sinha et al. | Aug 2013 | A1 |
| 20130290502 | Bilobrov | Oct 2013 | A1 |
| 20130297727 | Levy | Nov 2013 | A1 |
| 20130318096 | Cheung | Nov 2013 | A1 |
| 20140016696 | Nelson | Jan 2014 | A1 |
| 20140025837 | Swenson | Jan 2014 | A1 |
| 20140052737 | Ramanathan et al. | Feb 2014 | A1 |
| 20140078400 | Vajjhula | Mar 2014 | A1 |
| 20140082663 | Neumeier et al. | Mar 2014 | A1 |
| 20140088742 | Srinivasan | Mar 2014 | A1 |
| 20140105567 | Casagrande | Apr 2014 | A1 |
| 20140123165 | Mukherjee et al. | May 2014 | A1 |
| 20140130092 | Kunisetty | May 2014 | A1 |
| 20140153571 | Neugebauer | Jun 2014 | A1 |
| 20140188487 | Perez Gonzalez | Jul 2014 | A1 |
| 20140193027 | Scherf | Jul 2014 | A1 |
| 20140195548 | Harron | Jul 2014 | A1 |
| 20140196085 | Dunker | Jul 2014 | A1 |
| 20140201769 | Neumeier et al. | Jul 2014 | A1 |
| 20140201772 | Neumeier et al. | Jul 2014 | A1 |
| 20140201787 | Neumeier et al. | Jul 2014 | A1 |
| 20140219554 | Yamaguchi et al. | Aug 2014 | A1 |
| 20140237576 | Zhang | Aug 2014 | A1 |
| 20140258375 | Munoz | Sep 2014 | A1 |
| 20140270489 | Jaewhan et al. | Sep 2014 | A1 |
| 20140270504 | Baum et al. | Sep 2014 | A1 |
| 20140270505 | McCarthy | Sep 2014 | A1 |
| 20140282671 | McMillan | Sep 2014 | A1 |
| 20140293794 | Zhong et al. | Oct 2014 | A1 |
| 20140344880 | Geller et al. | Nov 2014 | A1 |
| 20150026728 | Carter et al. | Jan 2015 | A1 |
| 20150040074 | Hofmann et al. | Feb 2015 | A1 |
| 20150100979 | Moskowitz et al. | Apr 2015 | A1 |
| 20150112988 | Pereira et al. | Apr 2015 | A1 |
| 20150120839 | Kannan et al. | Apr 2015 | A1 |
| 20150121409 | Zhang et al. | Apr 2015 | A1 |
| 20150128161 | Conrad et al. | May 2015 | A1 |
| 20150131845 | Forouhar | May 2015 | A1 |
| 20150163545 | Freed et al. | Jun 2015 | A1 |
| 20150181311 | Navin et al. | Jun 2015 | A1 |
| 20150188967 | Paulauskas | Jul 2015 | A1 |
| 20150205727 | Kimmel | Jul 2015 | A1 |
| 20150229998 | Kaushal et al. | Aug 2015 | A1 |
| 20150256891 | Kim et al. | Sep 2015 | A1 |
| 20150302890 | Ergen et al. | Oct 2015 | A1 |
| 20150382075 | Neumeier et al. | Dec 2015 | A1 |
| 20160050445 | Baghdassarian | Feb 2016 | A1 |
| 20160073166 | Hu | Mar 2016 | A1 |
| 20160112319 | Hasani | Apr 2016 | A1 |
| 20160154880 | Hoarty | Jun 2016 | A1 |
| 20160227261 | Neumeier et al. | Aug 2016 | A1 |
| 20160227291 | Shaw et al. | Aug 2016 | A1 |
| 20160286244 | Chang et al. | Sep 2016 | A1 |
| 20160307043 | Neumeier | Oct 2016 | A1 |
| 20160314794 | Leitman et al. | Oct 2016 | A1 |
| 20160316262 | Chen | Oct 2016 | A1 |
| 20160330529 | Byers | Nov 2016 | A1 |
| 20160353172 | Miller et al. | Dec 2016 | A1 |
| 20160359791 | Zhang et al. | Dec 2016 | A1 |
| 20170017645 | Neumeier et al. | Jan 2017 | A1 |
| 20170017651 | Neumeier et al. | Jan 2017 | A1 |
| 20170017652 | Neumeier et al. | Jan 2017 | A1 |
| 20170019716 | Neumeier et al. | Jan 2017 | A1 |
| 20170019719 | Neumeier et al. | Jan 2017 | A1 |
| 20170026671 | Neumeier et al. | Jan 2017 | A1 |
| 20170031573 | Kaneko | Feb 2017 | A1 |
| 20170032033 | Neumeier et al. | Feb 2017 | A1 |
| 20170032034 | Neumeier et al. | Feb 2017 | A1 |
| 20170090814 | Yeung | Mar 2017 | A1 |
| 20170134770 | Neumeier et al. | May 2017 | A9 |
| 20170186042 | Wong et al. | Jun 2017 | A1 |
| 20170311014 | Fleischman | Oct 2017 | A1 |
| 20170325005 | Liassides | Nov 2017 | A1 |
| 20170353776 | Holden et al. | Dec 2017 | A1 |
| 20180227646 | Navin et al. | Aug 2018 | A9 |
| Number | Date | Country |
|---|---|---|
| 2501316 | Sep 2005 | CA |
| 1557096 | Dec 2004 | CN |
| 101162470 | Apr 2008 | CN |
| 1681304 | Jul 2010 | CN |
| 102377960 | Mar 2012 | CN |
| 101681373 | Sep 2012 | CN |
| 248 533 | Aug 1994 | EP |
| 1578126 | Sep 2005 | EP |
| 1 760 693 | Mar 2007 | EP |
| 1504445 | Aug 2008 | EP |
| 2 084 624 | Aug 2009 | EP |
| 2 352 289 | Aug 2011 | EP |
| 2 541 963 | Jan 2013 | EP |
| 2 685 450 | Jan 2014 | EP |
| 2457694 | Aug 2009 | GB |
| 0144992 | Jun 2001 | WO |
| 2005101998 | Nov 2005 | WO |
| 2007114796 | Oct 2007 | WO |
| 2008065340 | Jun 2008 | WO |
| 2009131861 | Oct 2009 | WO |
| 2009150425 | Dec 2009 | WO |
| 2010135082 | Nov 2010 | WO |
| 2011090540 | Jul 2011 | WO |
| 2012057724 | May 2012 | WO |
| 2012108975 | Aug 2012 | WO |
| 2012170451 | Dec 2012 | WO |
| 2014142758 | Sep 2014 | WO |
| 2014145929 | Sep 2014 | WO |
| 2015100372 | Jul 2015 | WO |
| 2016033676 | Mar 2016 | WO |
| 2016123495 | Aug 2016 | WO |
| 2016168556 | Oct 2016 | WO |
| 2017011758 | Jan 2017 | WO |
| 2017011792 | Jan 2017 | WO |
| Entry |
|---|
| International Search Report and Written Opinion dated Mar. 31, 2015 for PCT Application No. PCT/US2014/072255, 8 pages. |
| International Search Report and Written Opinion dated Apr. 26, 2016 for PCT Application No. PCT/US2016/015681, 13 pages. |
| “How to: Watch from the beginning |About DISH” (Dec. 31, 2014) XP055265764, retrieved on Apr. 15, 2016 from URL:http://about.dish.com/blog/hopper/how-watch-beginning 2 pages. |
| International Search Report and Written Opinion dated Jun. 24, 2016 for PCT Application No. PCT/US2016/027691, 13 pages. |
| Gionis et al., “Similarity Search in High Dimension via Hashing”, Proceedings of the 25th VLDB Conference, 1999, 12 pages. |
| Huang , “Bounded Coordinate System Indexing for Real-time Video Clip Search”, Retrieved from the Internet:URL:http://staff.itee.uq.edu.aujzxf/papers/TOIS.pdf, Jan. 1, 2009, 32 pages. |
| Kim et al., “Edge-Based Spatial Descriptor Using Color Vector Angle for Effective Image Retrieval”, Modeling Decisions for Artificial Intelligence; [Lecture Notes in Computer Science Lecture Notes in Artificial Intelligence, Jul. 1, 2005, pp. 365-375. |
| Liu et al., “Near-duplicate video retrieval”, ACM Computing Surveys, vol. 45, No. 4, Aug. 30, 2013, pp. 1-23. |
| International Search Report and Written Opinion dated Oct. 12, 2016 for PCT Application No. PCT/US2016/042522, 13 pages. |
| International Search Report and Written Opinion dated Oct. 11, 2016 for PCT Application No. PCT/US2016/042621, 13 pages. |
| International Search Report and Written Opinion dated Oct. 20, 2016 for PCT Application No. PCT/US2016/042611, 12 pages. |
| Scouarnec et al., “Cache policies for cloud-based systems:To keep or not to keep”, 2014 IEEE 7th International Conference on Cloud Computing, IEEE XP032696624, Jun. 27, 2014, pp. 1-8. |
| International Search Report and Written Opinion dated Oct. 25, 2016 for PCT Application No. PCT/US2016/042564, 14 pages. |
| Anonymous; “Cache (computing)” Wikipedia, the free encyclopedia, URL:http://en.wikipedia.org/w/index.phpti tle=Cache (computing)&oldid=474222804, Jan. 31, 2012; 6 pages. |
| International Search Report and Written Opinion dated Oct. 24, 2016 for PCT Application No. PCT/US2016/042557, 11 pages. |
| Anil K. Jain, “Image Coding via a Nearest Neighbors Image Model” IEEE Transactions on Communications, vol. Com-23, No. 3, Mar. 1975, pp. 318-331. |
| Lee et al., “Fast Video Search Algorithm for Large Video Database Using Adjacent Pixel Intensity Difference Quantization Histogram Feature” International Journal of Computer Science and Network Security, vol. 9, No. 9, Sep. 2009, pp. 214-220. |
| Li et al., A Confidence Based Recognition System for TV Commercial Extraction, Conferences in Research and Practice in Information Technology vol. 75, 2008. |
| International Search Report and Written Opinion dated Jul. 27, 2011 for PCT Application No. PCT/US2010/057153, 8 pages. |
| International Search Report and Written Opinion dated Aug. 31, 2011 for PCT Application No. PCT/US2010/057155, 8 pages. |
| International Search Report and Written Opinion dated Aug. 26, 2014 for PCT Application No. PCT/US2014/030782; 11 pages. |
| International Search Report and Written Opinion dated Jul. 21, 2014 for PCT Application No. PCT/US2014/030795; 10 pages. |
| International Search Report and Written Opinion, dated Jul. 25, 2014 for PCT Application No. PCT/US2014/030805, 10 pages. |
| Extended European Search Report dated Mar. 7, 2013 for European Application No. 12178359.1, 8 pages. |
| Extended European Search Report dated Oct. 11, 2013 for European Application No. 10844152.8, 19 pages. |
| Kabal (P.), Ramachandran (R.P.): The computation of line spectral frequencies using Chebyshev polynomials, IEEE Trans. on ASSP, vol. 34, No. 6, pp. 1419-1426, 1986. |
| Itakura (F.): Line spectral representation of linear predictive coefficients of speech signals, J. Acoust. Soc. Amer., vol. 57, Supplement No. 1, S35, 1975, 3 pages. |
| Bistritz (Y.), Pellerm (S.): Immittance Spectral Pairs (ISP) for speech encoding, Proc. ICASSP'93, pp. 11-19 to 11-12. |
| International Search Report and Written Opinion dated Mar. 8, 2016 for PCT Application No. PCT/ US2015/062945; 9 pages. |
| Extended European Search Report dated Dec. 21, 2016 for European Application No. 14763506.4, 11 pages. |
| Extended European Search Report dated Nov. 23, 2016 for European Application No. 14764182.3, 10 pages. |
| Extended European Search Report dated Jan. 24, 2017 for European Application No. 14762850.7, 12 pages. |
| Extended European Search Report dated Jun. 16, 2017, for European Patent Application No. 14873564.0, 8 pages. |
| Extended European Search Report dated Jun. 28, 2019, for European Patent Application No. 19166400.2, 9 pages. |
| U.S. Appl. No. 14/551,933, “Final Office Action”, dated May 23, 2016, 19 pages . |
| U.S. Appl. No. 14/551,933 , “Non-Final Office Action”, dated Oct. 17, 2016, 15 pages. |
| U.S. Appl. No. 14/551,933 , “Non-Final Office Action”, dated Dec. 31, 2015, 24 pages. |
| U.S. Appl. No. 14/551,933 , “Notice of Allowance”, dated Mar. 21, 2017, 8 pages. |
| U.S. Appl. No. 14/217,039 , “Non-Final Office Action”, dated May 23, 2014, 27 pages. |
| U.S. Appl. No. 14/217,039 , “Final Office Action”, dated Nov. 7, 2014, 15 pages. |
| U.S. Appl. No. 14/217,039 , “Notice of Allowance”, dated Jan. 29, 2015, 8 pages. |
| U.S. Appl. No. 14/678,856, “Non-Final Office Action”, dated Dec. 1, 2015, 28 pages. |
| U.S. Appl. No. 14/678,856, “Notice of Allowance”, dated May 20, 2016, 9 pages. |
| U.S. Appl. No. 14/217,075, “Non-Final Office Action”, dated Jul. 16, 2014, 39 pages. |
| U.S. Appl. No. 14/217,075, “Notice of Allowance ”, dated Feb. 20, 2015, 51 pages. |
| U.S. Appl. No. 14/217,094, “Notice of Allowance ”, dated Sep. 4, 2014, 30 pages. |
| U.S. Appl. No. 14/217,375, “Non-Final Office Action”, dated Apr. 1, 2015, 39 pages. |
| U.S. Appl. No. 14/217,375, “Notice of Allowance”, dated Apr. 1, 2015, 31 pages. |
| U.S. Appl. No. 14/217,425, “Non-Final Office Action”, dated Apr. 7, 2015, 12 pages. |
| U.S. Appl. No. 14/217,425, “Notice of Allowance”, dated May 20, 2015, 15 pages. |
| U.S. Appl. No. 14/217,435, “Non-Final Office Action”, dated Nov. 24, 2014, 9 pages. |
| U.S. Appl. No. 14/217,435, “Notice of Allowance”, dated Jun. 5, 2015, 9 pages. |
| U.S. Appl. No. 15/011,099 , “First Action Interview Office Action Summary”, dated May 9, 2017, 6 pages. |
| U.S. Appl. No. 15/011,099 , “First Action Interview Pilot Program Pre-Interview Communication”, dated Feb. 28, 2017, 5 pages. |
| U.S. Appl. No. 12/788,721 , “Non-Final Office Action”, dated Mar. 28, 2012, 15 Pages. |
| U.S. Appl. No. 12/788,721 , “Final Office Action”, dated Aug. 15, 2012, 22 Pages. |
| U.S. Appl. No. 12/788,721 , “Notice of Allowance”, dated Aug. 15, 2013, 16 Pages. |
| U.S. Appl. No. 14/763,158 , “Non-Final Office Action”, dated Jun. 27, 2016, 16 Pages. |
| U.S. Appl. No. 14/763,158 , “Final Office Action”, dated Sep. 7, 2016, 12 Pages. |
| U.S. Appl. No. 14/763,158 , “Notice of Allowance”, dated Mar. 17, 2016, 8 Pages. |
| U.S. Appl. No. 14/807,849 , “Non-Final Office Action”, dated Nov. 25, 2015, 12 Pages. |
| U.S. Appl. No. 14/807,849 , “Final Office Action”, dated Apr. 19, 2016, 13 pages. |
| U.S. Appl. No. 14/807,849 , “Non-Final Office Action”, dated Feb. 28, 2017, 10 Pages. |
| U.S. Appl. No. 14/089,003 , “Notice of Allowance”, dated Jul. 30, 2014, 24 Pages. |
| U.S. Appl. No. 12/788,748 , “Non-Final Office Action”, dated Jan. 10, 2013, 10 Pages. |
| U.S. Appl. No. 12/788,748 , “Final Office Action”, dated Nov. 21, 2013, 13 Pages. |
| U.S. Appl. No. 12/788,748 , “Notice of Allowance”, dated Mar. 6, 2014, 7 Pages. |
| U.S. Appl. No. 14/953,994 , “Non-Final Office Action”, dated Mar. 3, 2016, 34 Pages. |
| U.S. Appl. No. 14/953,994 , “Final Office Action”, dated Jun. 1, 2016, 36 Pages. |
| U.S. Appl. No. 14/953,994 , “Notice of Allowance”, dated Aug. 31, 2016, 15 Pages. |
| U.S. Appl. No. 14/807,849 , “Final Office Action”, dated Jun. 22, 2017, 10 pages. |
| U.S. Appl. No. 15/011,099 , “Final Office Action”, dated Jul. 24, 2017, 22 pages. |
| U.S. Appl. No. 15/240,801 , “Non-Final Office Action”, dated Aug. 11, 2017, 18 pages. |
| U.S. Appl. No. 15/240,815 , “Non-Final Office Action”, dated Aug. 23, 2017, 15 pages. |
| U.S. Appl. No. 15/211,345 , “First Action Interview Pilot Program Pre-Interview Communication”, dated Sep. 19, 2017, 8 pages. |
| U.S. Appl. No. 14/807,849 , “Notice of Allowance”, dated Nov. 30, 2017, 9 Pages. |
| U.S. Appl. No. 15/240,801 , “Final Office Action”, dated Dec. 22, 2017, 24 pages. |
| U.S. Appl. No. 15/011,099, “Non-Final Office Action”, dated Jan. 22, 2018, 23 pages. |
| U.S. Appl. No. 15/240,815 , “Final Office Action”, dated Mar. 2, 2018, 14 pages. |
| U.S. Appl. No. 15/211,345 , “Final Office Action”, dated Mar. 2, 2018, 14 pages. |
| Extended European Search Report dated Mar. 22, 2018 for European Application No. 15865033.3, 10 pages. |
| U.S. Appl. No. 15/099,842 , “Final Office Action”, dated Apr. 2, 2018, 8 pages. |
| U.S. Appl. No. 15/210,730, “Notice of Allowance”, dated May 23, 2018, 10 pages. |
| U.S. Appl. No. 15/796,706, “Non-Final Office Action”, dated Jun. 26, 2018, 17 pages. |
| U.S. Appl. No. 15/011,099, “Notice of Allowance”, dated Jun. 28, 2018, 12 pages. |
| U.S. Appl. No. 15/796,698, “Non-Final Office Action”, dated Jul. 5, 2018, 15 pages. |
| U.S. Appl. No. 15/240,801, “Notice of Allowance”, dated Aug. 30, 2018, 9 pages. |
| U.S. Appl. No. 15/211,345, “Non-Final Office Action”, dated Sep. 4, 2018, 13 pages. |
| U.S. Appl. No. 15/099,842, “Notice of Allowance”, dated Sep. 7, 2018, 10 pages. |
| U.S. Appl. No. 15/240,815, “Notice of Allowance”, dated Sep. 12, 2018, 9 pages. |
| U.S. Appl. No. 15/290,848 , “First Action Interview Office Action Summary”, dated Nov. 2, 2018, 5 pages. |
| U.S. Appl. No. 15/796,692, “Notice of Allowance”, dated Dec. 5, 2018, 8 pages. |
| U.S. Appl. No. 15/796,698, “Notice of Allowance”, dated Dec. 20, 2018, 8 pages. |
| U.S. Appl. No. 15/796,706, “Notice of Allowance”, dated Jan. 11, 2019, 9 pages. |
| U.S. Appl. No. 15/211,508, “Non-Final Office Action”, dated Jan. 10, 2019, 20 pages. |
| U.S. Appl. No. 15/211,492, “Non-Final Office Action”, dated Jan. 11, 2019, 19 pages. |
| U.S. Appl. No. 16/141,598, “First Action Interview Office Action Summary”, dated Jan. 11, 2019, 8 pages. |
| U.S. Appl. No. 15/290,848, “Non-Final Office Action”, dated Mar. 5, 2019, 5 pages. |
| U.S. Appl. No. 15/211,991, “Non-Final Office Action”, dated Feb. 26, 2019, 8 pages. |
| U.S. Appl. No. 15/211,345, “Notice of Allowance”, dated Mar. 20, 2019, 6 pages. |
| U.S. Appl. No. 16/210,796, “Non-Final Office Action”, dated Mar. 22, 2019, 7 pages. |
| U.S. Appl. No. 16/141,598, “Notice of Allowance”, dated Apr. 25, 2019, 11 pages. |
| U.S. Appl. No. 15/211,508, “Final Office Action”, dated Jun. 20, 2019, 20 pages. |
| U.S. Appl. No. 15/211,492, “Final Office Action”, dated Jun. 20, 2019, 18 pages. |
| U.S. Appl. No. 16/210,796, “Notice of Allowance”, dated Jul. 11, 2019, 9 pages. |
| U.S. Appl. No. 16/214,875, “Non-Final Office Action”, dated Jul. 29, 2019, 12 pages. |
| U.S. Appl. No. 15/211,991, “Final Office Action”, dated Oct. 31, 2019, 9 pages. |
| U.S. Appl. No. 16/214,875, “Final Office Action”, dated Jan. 29, 2020, 13 pages. |
| U.S. Appl. No. 16/290,055, “Non-Final Office Action”, dated Feb. 19, 2020, 18 pages. |
| U.S. Appl. No. 16/379,663, “Non-Final Office Action”, dated Feb. 24, 2020, 19 pages. |
| U.S. Appl. No. 15/211,991, “Non-Final Office Action”, dated Mar. 19, 2020, 19 pages. |
| U.S. Appl. No. 15/211,508, “Non-Final Office Action”, dated Apr. 3, 2020, 25 pages. |
| U.S. Appl. No. 15/211,492, “Non-Final Office Action”, dated Apr. 6, 2020, 18 pages. |
| U.S. Appl. No. 16/290,055, “Notice of Allowance”, dated Jun. 25, 2020, 10 pages. |
| U.S. Appl. No. 16/214,875, “Non-Final Office Action”, dated Jul. 15, 2020, 21 pages. |
| U.S. Appl. No. 16/379,663, “Final Office Action”, dated Jul. 28, 2020, 15 pages. |
| U.S. Appl. No. 16/444,152, “Notice of Allowance”, dated Aug. 19, 2020, 6 pages. |
| U.S. Appl. No. 15/211,991, “Notice of Allowance”, dated Sep. 17, 2020, 13 pages. |
| U.S. Appl. No. 15/211,492, “Final Office Action”, dated Oct. 26, 2020, 18 pages. |
| U.S. Appl. No. 15/211,508, “Notice of Allowance”, dated Oct. 27, 2020, 13 pages. |
| U.S. Appl. No. 16/519,292, “Notice of Allowance ”, dated Oct. 29, 2020, 10 pages. |
| U.S. Appl. No. 16/214,875, “Final Office Action”, dated Nov. 30, 2020, 17 pages. |
| U.S. Appl. No. 16/379,663, “Notice of Allowance”, dated Feb. 16, 2021, 9 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20210144450 A1 | May 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62193322 | Jul 2015 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16444152 | Jun 2019 | US |
| Child | 17099964 | US | |
| Parent | 15211345 | Jul 2016 | US |
| Child | 16444152 | US |
