US20070112567A1 - Techiques for model optimization for statistical pattern recognition - Google Patents
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Definitions
- Particular embodiments generally relate to statistical pattern recognition and more specifically to model optimization for statistical pattern recognition.
- Speech recognition involves transcribing spoken words into text automatically.
- speech recognition has been applied to dictation, conversational systems, and surveillance.
- the task of transcribing spoken words into text for these applications is very different.
- the speech recognition system is dealing with a large vocabulary but the acoustic and vocabulary variability is limited as the dictation system is typically dealing with a single speaker.
- the tolerance for error is low as the speed of dictation should exceed that of the typing of the user.
- the speech recognition is dealing with a small vocabulary with a small variability, such as asking users to answer very directed questions (e.g., yes or no questions).
- the acoustic variability is high as the speech recognition system needs to act with a variety of speakers.
- the tolerance for error is low as an error may lead to an incorrect transaction.
- the speech recognition is dealing with a lower vocabulary with a high degree of acoustic and vocabulary variability.
- the goal of the speech recognition system in surveillance is typically to reduce the amount of data that is manually processed, a high level of tolerance for error is allowed.
- models are developed for speech recognition systems depending on the application it is used for. These models are typically geared toward the different characteristics of the applications. Because these characteristics do not generally change for the application being used, the models are somewhat static and are generally the same based on the application. For example, models for dictation systems are generally the same because of the characteristics of information processed for this application does not significantly change.
- Particular embodiments generally relate to model optimization for a statistical pattern recognition engine.
- a statistical pattern recognition engine determines content for a statistical pattern recognition task.
- the content and/or information related to the content is analyzed to determine a model to use in the statistical pattern recognition.
- models may be classified in a plurality of domains based on different sets of data used to train models in the domain.
- the models may be classified based on knowledge sources used to generate the models, such as a news knowledge source, entertainment knowledge source, business knowledge source, etc.
- a model in the plurality of models is then determined based on the analysis where the determined model is classified in a domain.
- the model is then used by the statistical pattern recognition engine to perform the statistical pattern recognition task. For example, spoken words are transcribed into text using the determined model.
- a method for determining a model for a statistical pattern recognition engine comprises: determining content for analysis by the statistical pattern recognition engine; analyzing the content and/or information related to the content to determine a model in a plurality of models, wherein the determined model is classified in a domain in a plurality of domains, the domain including one or more models trained using information determined to include similar characteristics to the content; and providing the determined model to the statistical pattern recognition engine for statistical pattern recognition.
- a method for determining a model for a statistical pattern recognition engine comprises: receiving a plurality of files, each file including rich media content; for each of the files, performing the following: analyzing the content in the file and/or information related to the content to determine a domain that is determined to include similar characteristics to the content; determining a model in the determined domain based on the content in the file and/or information related to the content; and providing the determined model to the statistical pattern recognition engine for analysis.
- an apparatus configured to determine a model for a statistical pattern recognition engine.
- the apparatus comprises: one or more processors; and logic encoded in one or more tangible media for execution by the one or more processors and when executed operable to: determine content for analysis by the statistical pattern recognition engine; analyze the content and/or information related to the content to determine a model in a plurality of models, wherein the determined model is classified in a domain in a plurality of domains, the domain including one or more models trained using information determined to include similar characteristics to the content; and provide the determined model to the statistical pattern recognition engine for statistical pattern recognition.
- an apparatus configured to determine a model for a statistical pattern recognition engine.
- the apparatus comprises: one or more processors; and logic encoded in one or more tangible media for execution by the one or more processors and when executed operable to: receive a plurality of files, each file including rich media content; for each of the files, the logic when executed is further operable to: analyze the content in the file and/or information related to the content to determine a domain that is determined to include similar characteristics to the content; determine a model in the determined domain based on the content in the file and/or information related to the content; and provide the determined model to the statistical pattern recognition engine for analysis.
- FIG. 1 depicts an example of a statistical pattern recognition system according to one embodiment of the present invention.
- FIG. 2 shows an example of models according to one embodiment of the present invention.
- FIG. 3 depicts an example of a statistical pattern recognition system according to one embodiment of the present invention.
- FIG. 4 shows an example of determining a model for an acoustic model according to one embodiment of the present invention.
- FIG. 5 depicts a second example of determining a model for a language model according to one embodiment of the present invention.
- FIG. 6 depicts a simplified flowchart of a method for performing statistical pattern recognition according to one embodiment of the present invention.
- FIG. 1 depicts an example of a statistical pattern recognition system 100 according to one embodiment of the present invention.
- statistical pattern recognition system 100 includes a statistical pattern recognition engine 102 and a model optimizer 104 .
- Statistical pattern recognition engine 102 is configured to analyze content using models.
- the engine is a statistical machine that uses statistical models to transcribe some pattern into a target.
- statistical pattern engine may transcribe spoken words to text for speech recognition.
- Statistical pattern recognition engine 102 matches content against statistical models, such as acoustic, language, and/or semantic models to find the best match of words in the content's audio stream.
- the statistical models may be any model that is generated from source data.
- the content may be any information, such as video, audio, conversations, surveillance information, or any other information that includes spoken words. Examples of content include webcasts, podcasts, commercials, TV shows, etc.
- Acoustic models capture the way phonemes sound.
- the language models capture the way phonemes combine to form words and the way words combine to form phrases.
- Different types of models require different types of data. For example, acoustic models require data in the form of voice samples and associated transcripts.
- Language models require data in the form of text.
- the performance and accuracy of statistical pattern recognition engine 102 may depend on how well the models used match the content being recognized. For example, acoustic models trained with noisy voice samples may work best with noisy content. Language models trained with speech text may not work very well with conversational speech. Rather, models trained with conversational speech may work better.
- model optimizer 104 is configured to determine a model to use based on the content received. Different models may be trained that have different characteristics. The different models may be partitioned into different domains. A domain may be based on different sets of data that are used to generate the model. For example, a domain may be defined as essentially a space that is similar by some metric. A news domain may have a news subject similarity, business domain may have a business subject similarity, entertainment domain may have an entertainment subject similarity, etc. Similarly, in the acoustic model case, it is acoustic/spectral similarity and in the language model case, it is topical similarity. Other domains may also be appreciated.
- the models in the different domains are built using different knowledge sources.
- a model in a business domain is created by using data from various business web sites.
- news reports from different news anchors on a web site, such as CNN.com are used to train one or more models in a news domain.
- One of these models may be used when statistical pattern recognition of content from CNN is performed.
- the content may have similar characteristics to the data used to train the models from CNN because news content may have similar characteristics.
- newscasts typically follow the same format, i.e., a newscaster is reading a story.
- a model trained with these characteristics may yield better statistical pattern recognition results than a model trained using people conversing in a conversational manner.
- model optimizer 104 is configured to dynamically determine a model to use. The determination may be based on analysis of the content and/or information related to the content, such as metadata. Metadata may include a source of the content, a description of the content, data it was available, etc. Other examples of metadata may also be appreciated. In one example, if the content is news-based, then a model in a news domain may be chosen as the model to use for statistical pattern recognition engine 102 . Although only one model is discussed as being selected, it will be understood that any number of models may be selected and used in a statistical pattern recognition analysis.
- model optimizer 104 may modify the model to include data that is associated with the news story, such as a name of a person that is the subject of the story. This may be useful if the name of the person is not a popular name that may not have been used in the training of the model. Also, other characteristics in the model may be altered based on the content.
- statistical pattern recognition engine 102 can perform the statistical pattern recognition with the content. For example, statistical pattern recognition engine 102 may transcribe spoken words in the content to text using the determined model. The content may be received and statistical pattern recognition engine 102 may output the associated text for the spoken words with a time stamp for when the spoken words are spoken.
- FIG. 2 shows an example of models according to one embodiment of the present invention. As shown, models 204 are partitioned into one or more domains. A model data determiner 202 is used to determine domain-specific information that is used to generate and update the models with training data.
- a domain may be any information that is associated with a knowledge source.
- specific domain models may be generated from knowledge sources in the entertainment, news, business, etc., domains.
- Each domain may be associated with any number of models 204 .
- a domain may be associated with a single model or there may be multiple models in a domain.
- Model data determiner 202 is configured to determine information. Automatic gatherers of information may be used to determine information. For example, spiders may be used to search various web sites to determine information. This information may or may not be determined from rich media content. For example, a spider may determine words that are popular from text web sites or from video newscasts. Popular news stories may be determined and the keywords used in those stories are determined as words that may be more likely spoken. These words may be used to train the models in a domain.
- Model data determiner 202 may then classify the information in various domains. For example, classification may be determined by measuring similarity to a domain. Model data determiner 202 may determine that information is news related and thus should be classified in the news domain. This may be determined using tags in the metadata, i.e., if the metadata says the information is news, or the metadata says the information is from cnn.com, then the information is classified in the news model. In other examples, the information is analyzed to determine the classification. For example, for acoustic models, the SNR is computed from the information to classify it.
- knowledge sources are determined for various domains.
- the news domain may be associated with news knowledge sources and an entertainment domain may be associated with entertainment knowledge sources.
- a CNN website may be used as a knowledge source.
- a spider may search the CNN website to determine domain-specific information.
- the CNN web site may include various types of information, such as video newscasts, web page articles, etc. Characteristics of speakers may be determined. These characteristics may apply over content classified in the domain because different newscasts may include similar characteristics.
- optimization of statistical pattern recognition may be provided because models have been optimized for specific subject matter. For example, if a model is trained with information from a news web site, it may be more relevant for news content. This is because characteristics for content in the news domain may be more like the training data culled from news knowledge sources. For example, the terms used, such as specific names for news stories, the speaking style, etc., may be better suited for news content.
- dynamic updater 206 is configured to update models in the domain. Not all of the models may be updated in the same way as each model may be different. In one example, if a new news anchor is being used in the CNN website, the acoustics for the speaker are determined and an acoustic model 204 is then updated with the new acoustic information.
- Updates to the models in a domain may be performed at various intervals.
- the models 204 may be updated daily, hourly, in real-time, etc.
- the updating of the models provides more accurate models that optimize the statistical pattern recognition. For example, as a news story breaks and may become more popular, data for that news story may be determined and a model may be updated. Thus, when content for the news story is received by statistical pattern recognition engine 102 , then the updated models 204 may be used to optimize the results of statistical pattern recognition of the content. Extensions to models 204 may also be used in lieu of updating the models.
- statistical pattern recognition of content will now be described.
- the statistical pattern recognition of content may be performed for any reasons.
- statistical pattern recognition of content is performed to transcribe rich media content into text for the purpose of allowing searches of video content. These searches of video content are described in more detail in U.S. patent application Ser. No. ______, entitled “TECHNIQUES FOR RENDERING ADVERTISMENTS WITH RICH MEDIA”, filed concurrently, and incorporated by reference in its entirety for all purposes.
- Statistical pattern recognition of different kinds of content requires different models to be used to optimize the statistical pattern recognition. This is different from applications, such as dictation, which typically deal with content that has similar characteristics. For example, it is expected that different users using a dictation application will always be dictating in a similar style has similar characteristics.
- statistical pattern recognition system 100 may be processing many different kinds of content that have different characteristics. Accordingly, the partitioning of models into domains classifies the models such that they are trained with similar characteristics for content being processed. Further, the dynamic determination of a domain/model for the content received provides better results when a large number of pieces of content are being processed.
- FIG. 3 depicts a more detailed example of statistical pattern recognition system 100 according to one embodiment of the present invention.
- statistical pattern recognition engine 102 includes a domain determiner 302 , a model determiner 304 , and a model adapter 306 .
- Domain determiner 302 receives content and information related to the content. Domain determiner 302 analyzes the content and information related to the content to determine the appropriate domain for the content. For example, classification may be determined by measuring similarity to a domain. For example, the information related to the content may indicate that the content is from a news source, such as Gannett. Domain determiner 302 may then determine that the content received is news and thus should be classified in the news domain. Other methods of determining a domain may also be appreciated. For example, analysis of the content may indicate that it is a newscast and it is classified in the news domain. Further, the content may be analyzed to determine the domain, e.g., the SNR is computed from the content to determine the domain.
- the SNR is computed from the content to determine the domain.
- Model determiner 304 is then configured to determine a model 204 .
- model determiner 304 determines a model 204 that is in the domain that was determined by domain determiner 302 .
- domain determiner 302 the function of determining a domain and then determining a model the domain is described, it will be understood that separately determining of domain and then model may not be performed. Rather, a model may just be determined or if a single model is associated with a single domain, then just a domain may be determined.
- Model determiner 304 may determine the model in the domain using information about the content or information related to the content. For example, model determiner 304 may calculate characteristics from the content and compare them to characteristics for the models. The model that best matches the calculated characteristics may then be selected. For example, the model with the closest signal to noise ratio (SNR) to a SNR calculated from the content in the domain may be selected.
- SNR signal to noise ratio
- Model adapter 306 is configured to adapt the determined model 204 , as necessary. Model adapter 306 may adapt the determined model 204 based on the content and/or information related to the content. For example, if the content is about a specific person, the model may be adapted in include that person's name. Also, if the content includes a specific newscaster, then the acoustic characteristics for the model may be adjusted based on the newscaster.
- Model adapter 306 then outputs an optimized model for statistical pattern recognition engine 102 . Text and time stamps for spoken words in the content are then determined.
- model determiner 304 determines a model to use. Examples of determining models for content will now be described.
- FIG. 4 shows an example of determining a model for an acoustic model according to one embodiment of the present invention.
- model determiner 304 receives content and information related to the content. Model determiner 304 then determines a model 204 . In one example, it is assumed a domain has been determined and models 204 may be chosen from the domain.
- models 204 may be trained with data that is partitioned according to signal-to-noise ratio (SNR) and pitch of voice samples as well as sex of the speaker in the samples.
- SNR signal-to-noise ratio
- the data may be partitioned according to the origin of the data, i.e. a certain web site.
- One or more knowledge sources may be used to train models 204 .
- Model determiner 304 determines the signal-to-noise ratio and pitch of the content. Model determiner 304 then determines a model 204 based on the SNR and pitch of the content. For example, the model closest to the computed SNR and pitch is determined. Also, multiple models that are closest to the computed SNR and pitch may be determined. If the SNR and/or pitch cannot be computed reliably and/or the variance for the SNR/pitch is too high, model determiner 304 may use a generic model.
- model adapter 306 may adapt it.
- Model adapter 204 receives the content and information related to the content in addition to the determined model 204 . It can then adapt the model 204 based on the content. For example, using methods such as maximum likelihood linear regression (MLLR), model adapter 306 adapts the model for the content. For example, the model is adapted based on characteristics for the speaker in the content.
- MLLR maximum likelihood linear regression
- FIG. 5 depicts a second example of determining a model for a language model according to one embodiment of the present invention.
- models 204 in the different domains may be broken into more specific models, such as style models 502 and time-sensitive domain extension models 504 .
- Style models 502 are associated with speaking styles, such as reading style or conversational style.
- a reading style is when someone is reading content, such as in a newscast, and the conversational style is when people are conversing, such as holding a debate on a subject.
- the content may be analyzed to determine which specific speaking style is being used.
- the time-sensitive domain extension models 504 are built at regular intervals using the latest information, such as text, culled from various knowledge sources. Although this is shown as a separate model, it will be understood that a model may be updated itself. Time-sensitive domain extensions are used to capture any information that may become relevant for a particular domain at certain points in time. For example, the text may come from monitoring RSS feeds, a webcrawler, or any other knowledge source. When a news story breaks, the names of the persons associated with the story may be determined as extensions to models in the domain.
- model determiner 304 determines a model 204 by determining the most appropriate domain along with the speaking style model 502 , and time sensitive domain extension model 504 to use.
- Model adapter 306 then may adapt the models based on the content.
- FIG. 6 depicts a simplified flowchart 600 of a method for performing statistical pattern recognition according to one embodiment of the present invention.
- Step 602 determines the content for statistical pattern recognition. For example, content that may be later searched for using a search engine is used.
- Step 604 analyzes the content and/or information related to the content to determine a domain.
- the domain may be one out of a plurality of domains where the domain may be based on different sets of data from knowledge sources that are related to the domain. Models of the domain may be generated using different characteristics from knowledge sources.
- Step 606 determines a model associated with the domain.
- the model is determined based on the content and/or information related to the content.
- normalization of data culled from various knowledge sources may be performed.
- the raw data from knowledge sources such as web sites, may not be directly usable for training in models.
- the raw data is normalized.
- the normalization may be changing the sampling rate of audio.
- audio on the web may be captured at a sampling rate of 48 kHz but the acoustic model requires the model to be in 16 kHz.
- the audio captured may then be performed using standard down-sampling techniques.
- unprocessed raw HTML hypertext transfer markup language
- a training algorithm may only be able to take in sentences and prose within the HTML and not the symbols that are used in the language.
- the layout and the content of the HTML is intertwined within a file.
- a process may be used to determine the text/prose out of the HTML. For example, the process determines header patterns, footer patterns, etc. and uses trainable rules that can find beginning and ending of real content (as opposed to layout information). Normalization of text and language model training may also involve a set of rules that take into account document, word, sentence level constraints, in any order.
- models may be determined that are optimized for content received. These models may be tailored toward the content because they are associated with a domain related to the content. Further, the models may be updated periodically. Thus, content for recent events may use models that have been recently updated. Additionally, the models determined may be adapted on the content received. Accordingly, this provides better statistical pattern recognition of the content.
- routines of embodiments of the present invention can be implemented using C, C++, Java, assembly language, etc.
- Different programming techniques can be employed such as procedural or object oriented.
- the routines can execute on a single processing device or multiple processors. Although the steps, operations, or computations may be presented in a specific order, this order may be changed in different embodiments. In some embodiments, multiple steps shown as sequential in this specification can be performed at the same time.
- the sequence of operations described herein can be interrupted, suspended, or otherwise controlled by another process, such as an operating system, kernel, etc.
- the routines can operate in an operating system environment or as stand-alone routines occupying all, or a substantial part, of the system processing. Functions can be performed in hardware, software, or a combination of both. Unless otherwise stated, functions may also be performed manually, in whole or in part.
- a “computer-readable medium” for purposes of embodiments of the present invention may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, system or device.
- the computer readable medium can be, by way of example only but not by limitation, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, system, device, propagation medium, or computer memory.
- Embodiments of the present invention can be implemented in the form of control logic in software or hardware or a combination of both.
- the control logic may be stored in an information storage medium, such as a computer-readable medium, as a plurality of instructions adapted to direct an information processing device to perform a set of steps disclosed in embodiments of the present invention.
- an information storage medium such as a computer-readable medium
- a person of ordinary skill in the art will appreciate other ways and/or methods to implement the present invention.
- a “processor” or “process” includes any human, hardware and/or software system, mechanism or component that processes data, signals or other information.
- a processor can include a system with a general-purpose central processing unit, multiple processing units, dedicated circuitry for achieving functionality, or other systems. Processing need not be limited to a geographic location, or have temporal limitations. For example, a processor can perform its functions in “real time,” “offline,” in a “batch mode,” etc. Portions of processing can be performed at different times and at different locations, by different (or the same) processing systems.
- Embodiments of the invention may be implemented by using a programmed general purpose digital computer, by using application specific integrated circuits, programmable logic devices, field programmable gate arrays, optical, chemical, biological, quantum or nanoengineered systems, components and mechanisms may be used.
- the functions of embodiments of the present invention can be achieved by any means as is known in the art.
- Distributed, or networked systems, components and circuits can be used.
- Communication, or transfer, of data may be wired, wireless, or by any other means.
- any signal arrows in the drawings/ Figures should be considered only as exemplary, and not limiting, unless otherwise specifically noted.
- the term “or” as used herein is generally intended to mean “and/or” unless otherwise indicated. Combinations of components or steps will also be considered as being noted, where terminology is foreseen as rendering the ability to separate or combine is unclear.
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Cited By (20)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060242016A1 (en) * | 2005-01-14 | 2006-10-26 | Tremor Media Llc | Dynamic advertisement system and method |
US20070112630A1 (en) * | 2005-11-07 | 2007-05-17 | Scanscout, Inc. | Techniques for rendering advertisments with rich media |
US20080109391A1 (en) * | 2006-11-07 | 2008-05-08 | Scanscout, Inc. | Classifying content based on mood |
US20080270129A1 (en) * | 2005-02-17 | 2008-10-30 | Loquendo S.P.A. | Method and System for Automatically Providing Linguistic Formulations that are Outside a Recognition Domain of an Automatic Speech Recognition System |
US20090083417A1 (en) * | 2007-09-18 | 2009-03-26 | John Hughes | Method and apparatus for tracing users of online video web sites |
US20090138332A1 (en) * | 2007-11-23 | 2009-05-28 | Dimitri Kanevsky | System and method for dynamically adapting a user slide show presentation to audience behavior |
US20090204243A1 (en) * | 2008-01-09 | 2009-08-13 | 8 Figure, Llc | Method and apparatus for creating customized text-to-speech podcasts and videos incorporating associated media |
US20090259552A1 (en) * | 2008-04-11 | 2009-10-15 | Tremor Media, Inc. | System and method for providing advertisements from multiple ad servers using a failover mechanism |
US20110029666A1 (en) * | 2008-09-17 | 2011-02-03 | Lopatecki Jason | Method and Apparatus for Passively Monitoring Online Video Viewing and Viewer Behavior |
US20110093783A1 (en) * | 2009-10-16 | 2011-04-21 | Charles Parra | Method and system for linking media components |
US20110125573A1 (en) * | 2009-11-20 | 2011-05-26 | Scanscout, Inc. | Methods and apparatus for optimizing advertisement allocation |
US20110202411A1 (en) * | 2008-11-04 | 2011-08-18 | Kentaro Nakai | Advertising voice control device, integrated circuit, advertising voice control method, advertising voice control program, and recording medium |
US20120245934A1 (en) * | 2011-03-25 | 2012-09-27 | General Motors Llc | Speech recognition dependent on text message content |
US20150262575A1 (en) * | 2012-06-28 | 2015-09-17 | Nuance Communications, Inc. | Meta-data inputs to front end processing for automatic speech recognition |
US20160189730A1 (en) * | 2014-12-30 | 2016-06-30 | Iflytek Co., Ltd. | Speech separation method and system |
US9595258B2 (en) | 2011-04-04 | 2017-03-14 | Digimarc Corporation | Context-based smartphone sensor logic |
US9612995B2 (en) | 2008-09-17 | 2017-04-04 | Adobe Systems Incorporated | Video viewer targeting based on preference similarity |
WO2018117532A1 (fr) * | 2016-12-19 | 2018-06-28 | Samsung Electronics Co., Ltd. | Procédé et appareil de reconnaissance vocale |
US11049094B2 (en) | 2014-02-11 | 2021-06-29 | Digimarc Corporation | Methods and arrangements for device to device communication |
US20220318853A1 (en) * | 2019-05-08 | 2022-10-06 | Data Vault Holdings, Inc. | System and method for tokenized licensing of content |
Families Citing this family (151)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001084536A1 (fr) * | 2000-04-28 | 2001-11-08 | Deutsche Telekom Ag | Procede de calcul d'une decision d'activite vocale (detecteur d'activite vocale) |
US20090112715A1 (en) * | 2007-10-31 | 2009-04-30 | Ryan Steelberg | Engine, system and method for generation of brand affinity content |
US20090112698A1 (en) * | 2007-10-31 | 2009-04-30 | Ryan Steelberg | System and method for brand affinity content distribution and optimization |
US20090024409A1 (en) * | 2002-02-06 | 2009-01-22 | Ryan Steelberg | Apparatus, system and method for a brand affinity engine using positive and negative mentions |
US20090018922A1 (en) * | 2002-02-06 | 2009-01-15 | Ryan Steelberg | System and method for preemptive brand affinity content distribution |
US20090228354A1 (en) * | 2008-03-05 | 2009-09-10 | Ryan Steelberg | Engine, system and method for generation of brand affinity content |
US20090112692A1 (en) * | 2007-10-31 | 2009-04-30 | Ryan Steelberg | Engine, system and method for generation of brand affinity content |
US20050283795A1 (en) * | 2004-05-14 | 2005-12-22 | Ryan Steelberg | Broadcast monitoring system and method |
US7673017B2 (en) | 2005-09-06 | 2010-03-02 | Interpolls Network Inc. | Systems and methods for integrating XML syndication feeds into online advertisement |
AU2006329833A1 (en) * | 2005-12-15 | 2007-07-05 | Google, Inc. | Content depot |
US20070239534A1 (en) * | 2006-03-29 | 2007-10-11 | Hongche Liu | Method and apparatus for selecting advertisements to serve using user profiles, performance scores, and advertisement revenue information |
US20090024922A1 (en) * | 2006-07-31 | 2009-01-22 | David Markowitz | Method and system for synchronizing media files |
US20080066107A1 (en) | 2006-09-12 | 2008-03-13 | Google Inc. | Using Viewing Signals in Targeted Video Advertising |
US8073681B2 (en) | 2006-10-16 | 2011-12-06 | Voicebox Technologies, Inc. | System and method for a cooperative conversational voice user interface |
KR100916717B1 (ko) * | 2006-12-11 | 2009-09-09 | 강민수 | 플레이 되고 있는 동영상 내용 맞춤형 광고 콘텐츠 제공방법 및 그 시스템 |
US20080162454A1 (en) * | 2007-01-03 | 2008-07-03 | Motorola, Inc. | Method and apparatus for keyword-based media item transmission |
US20080172359A1 (en) * | 2007-01-11 | 2008-07-17 | Motorola, Inc. | Method and apparatus for providing contextual support to a monitored communication |
US7818176B2 (en) | 2007-02-06 | 2010-10-19 | Voicebox Technologies, Inc. | System and method for selecting and presenting advertisements based on natural language processing of voice-based input |
US20080221995A1 (en) * | 2007-03-10 | 2008-09-11 | Jayant Kadambi | Method and system for associating rich content with a rich media content |
US8103646B2 (en) * | 2007-03-13 | 2012-01-24 | Microsoft Corporation | Automatic tagging of content based on a corpus of previously tagged and untagged content |
US7899869B1 (en) | 2007-03-22 | 2011-03-01 | Google Inc. | Broadcasting in chat system without topic-specific rooms |
US20080255686A1 (en) * | 2007-04-13 | 2008-10-16 | Google Inc. | Delivering Podcast Content |
US7889724B2 (en) * | 2007-04-13 | 2011-02-15 | Wideorbit, Inc. | Multi-station media controller |
US8667532B2 (en) | 2007-04-18 | 2014-03-04 | Google Inc. | Content recognition for targeting video advertisements |
US8078468B2 (en) * | 2007-05-21 | 2011-12-13 | Sony Ericsson Mobile Communications Ab | Speech recognition for identifying advertisements and/or web pages |
US20090217186A1 (en) * | 2008-02-27 | 2009-08-27 | Nokia Corporation | Apparatus, computer-readable storage medium and method for providing widgets including advertisements for associated widgets |
CN101682649A (zh) * | 2007-05-25 | 2010-03-24 | 诺基亚公司 | 用于提供包括针对关联窗件的广告的窗件的网络实体、终端、计算机可读存储介质和方法 |
US9569230B2 (en) * | 2007-05-25 | 2017-02-14 | Nokia Technologies Oy | Network entity, terminal, computer-readable storage medium and method for providing widgets including advertisements for associated widgets |
US20080306999A1 (en) * | 2007-06-08 | 2008-12-11 | Finger Brienne M | Systems and processes for presenting informational content |
US20080306824A1 (en) * | 2007-06-08 | 2008-12-11 | Parkinson David C | Empty Space Advertising Engine |
US8433611B2 (en) * | 2007-06-27 | 2013-04-30 | Google Inc. | Selection of advertisements for placement with content |
WO2009011917A1 (fr) * | 2007-07-18 | 2009-01-22 | Fox Interactive Media | Système et procédé pour déployer un gadget logiciel de publicité |
US9064024B2 (en) | 2007-08-21 | 2015-06-23 | Google Inc. | Bundle generation |
US8639714B2 (en) * | 2007-08-29 | 2014-01-28 | Yahoo! Inc. | Integrating sponsored media with user-generated content |
US20090112718A1 (en) * | 2007-10-31 | 2009-04-30 | Ryan Steelberg | System and method for distributing content for use with entertainment creatives |
US8285700B2 (en) | 2007-09-07 | 2012-10-09 | Brand Affinity Technologies, Inc. | Apparatus, system and method for a brand affinity engine using positive and negative mentions and indexing |
US9633505B2 (en) * | 2007-09-07 | 2017-04-25 | Veritone, Inc. | System and method for on-demand delivery of audio content for use with entertainment creatives |
US20100131357A1 (en) * | 2007-09-07 | 2010-05-27 | Ryan Steelberg | System and method for controlling user and content interactions |
US20100131337A1 (en) * | 2007-09-07 | 2010-05-27 | Ryan Steelberg | System and method for localized valuations of media assets |
US20100114704A1 (en) * | 2007-09-07 | 2010-05-06 | Ryan Steelberg | System and method for brand affinity content distribution and optimization |
US7809603B2 (en) * | 2007-09-07 | 2010-10-05 | Brand Affinity Technologies, Inc. | Advertising request and rules-based content provision engine, system and method |
US20110047050A1 (en) * | 2007-09-07 | 2011-02-24 | Ryan Steelberg | Apparatus, System And Method For A Brand Affinity Engine Using Positive And Negative Mentions And Indexing |
US8725563B2 (en) * | 2007-09-07 | 2014-05-13 | Brand Affinity Technologies, Inc. | System and method for searching media assets |
US20100030746A1 (en) * | 2008-07-30 | 2010-02-04 | Ryan Steelberg | System and method for distributing content for use with entertainment creatives including consumer messaging |
US8452764B2 (en) * | 2007-09-07 | 2013-05-28 | Ryan Steelberg | Apparatus, system and method for a brand affinity engine using positive and negative mentions and indexing |
US20090112717A1 (en) * | 2007-10-31 | 2009-04-30 | Ryan Steelberg | Apparatus, system and method for a brand affinity engine with delivery tracking and statistics |
US20100114701A1 (en) * | 2007-09-07 | 2010-05-06 | Brand Affinity Technologies, Inc. | System and method for brand affinity content distribution and optimization with charitable organizations |
US20100318375A1 (en) * | 2007-09-07 | 2010-12-16 | Ryan Steelberg | System and Method for Localized Valuations of Media Assets |
US20090112714A1 (en) * | 2007-10-31 | 2009-04-30 | Ryan Steelberg | Engine, system and method for generation of brand affinity content |
US20100274644A1 (en) * | 2007-09-07 | 2010-10-28 | Ryan Steelberg | Engine, system and method for generation of brand affinity content |
US20090112700A1 (en) * | 2007-10-31 | 2009-04-30 | Ryan Steelberg | System and method for brand affinity content distribution and optimization |
US20100114719A1 (en) * | 2007-09-07 | 2010-05-06 | Ryan Steelberg | Engine, system and method for generation of advertisements with endorsements and associated editorial content |
US9294727B2 (en) * | 2007-10-31 | 2016-03-22 | Veritone, Inc. | System and method for creation and management of advertising inventory using metadata |
US20100131085A1 (en) * | 2007-09-07 | 2010-05-27 | Ryan Steelberg | System and method for on-demand delivery of audio content for use with entertainment creatives |
US20110078003A1 (en) * | 2007-09-07 | 2011-03-31 | Ryan Steelberg | System and Method for Localized Valuations of Media Assets |
US20100217664A1 (en) * | 2007-09-07 | 2010-08-26 | Ryan Steelberg | Engine, system and method for enhancing the value of advertisements |
US8751479B2 (en) * | 2007-09-07 | 2014-06-10 | Brand Affinity Technologies, Inc. | Search and storage engine having variable indexing for information associations |
US20100312711A1 (en) * | 2007-09-07 | 2010-12-09 | Ryan Steelberg | System And Method For On-Demand Delivery Of Audio Content For Use With Entertainment Creatives |
US20110040648A1 (en) * | 2007-09-07 | 2011-02-17 | Ryan Steelberg | System and Method for Incorporating Memorabilia in a Brand Affinity Content Distribution |
WO2009039182A1 (fr) * | 2007-09-17 | 2009-03-26 | Interpols Network Incorporated | Systèmes et procédés pour une distribution publicitaire à des tiers de gadgets logiciels internet |
US20090083145A1 (en) * | 2007-09-26 | 2009-03-26 | Microsoft Corporation | Dynamic hosted advertising supporting multiple formats |
US20090119169A1 (en) * | 2007-10-02 | 2009-05-07 | Blinkx Uk Ltd | Various methods and apparatuses for an engine that pairs advertisements with video files |
US20090089830A1 (en) * | 2007-10-02 | 2009-04-02 | Blinkx Uk Ltd | Various methods and apparatuses for pairing advertisements with video files |
US20110106632A1 (en) * | 2007-10-31 | 2011-05-05 | Ryan Steelberg | System and method for alternative brand affinity content transaction payments |
US20090299837A1 (en) * | 2007-10-31 | 2009-12-03 | Ryan Steelberg | System and method for brand affinity content distribution and optimization |
US20100076866A1 (en) * | 2007-10-31 | 2010-03-25 | Ryan Steelberg | Video-related meta data engine system and method |
US8189963B2 (en) * | 2007-11-13 | 2012-05-29 | Microsoft Corporation | Matching advertisements to visual media objects |
US10600082B1 (en) * | 2007-12-05 | 2020-03-24 | Beats Music, Llc | Advertising selection |
US8140335B2 (en) | 2007-12-11 | 2012-03-20 | Voicebox Technologies, Inc. | System and method for providing a natural language voice user interface in an integrated voice navigation services environment |
US20090157500A1 (en) * | 2007-12-15 | 2009-06-18 | Yahoo! Inc. | Advanced advertisements |
US9043828B1 (en) | 2007-12-28 | 2015-05-26 | Google Inc. | Placing sponsored-content based on images in video content |
US8190479B2 (en) | 2008-02-01 | 2012-05-29 | Microsoft Corporation | Video contextual advertisements using speech recognition |
JP2011511384A (ja) * | 2008-02-07 | 2011-04-07 | ブランド・アフィニティー・テクノロジーズ・インコーポレイテッド | キーワードを用いたブランドをレーティングするための質的な及び量的な方法 |
US9817822B2 (en) * | 2008-02-07 | 2017-11-14 | International Business Machines Corporation | Managing white space in a portal web page |
US8510661B2 (en) * | 2008-02-11 | 2013-08-13 | Goldspot Media | End to end response enabling collection and use of customer viewing preferences statistics |
US9824372B1 (en) | 2008-02-11 | 2017-11-21 | Google Llc | Associating advertisements with videos |
US7958156B2 (en) * | 2008-02-25 | 2011-06-07 | Yahoo!, Inc. | Graphical/rich media ads in search results |
US20090216634A1 (en) * | 2008-02-27 | 2009-08-27 | Nokia Corporation | Apparatus, computer-readable storage medium and method for providing a widget and content therefor |
JP4962986B2 (ja) * | 2008-04-01 | 2012-06-27 | ヤフー株式会社 | コンテンツデータをカテゴリに分類する方法、サーバ、およびプログラム |
US20090265665A1 (en) * | 2008-04-16 | 2009-10-22 | Stephen Martiros | Methods and apparatus for interactive advertising |
US20090265226A1 (en) * | 2008-04-16 | 2009-10-22 | Stephen Martiros | Methods and apparatus for interactive advertising |
US20090271261A1 (en) * | 2008-04-24 | 2009-10-29 | Neerav Mehta | Policy driven customer advertising |
US8650094B2 (en) | 2008-05-07 | 2014-02-11 | Microsoft Corporation | Music recommendation using emotional allocation modeling |
US8344233B2 (en) | 2008-05-07 | 2013-01-01 | Microsoft Corporation | Scalable music recommendation by search |
US10296920B2 (en) * | 2008-05-21 | 2019-05-21 | Wenxuan Tonnison | Online E-commerce and networking system/generating user requested sponsor advertisements to centralize siloed and distributed user data in the internet and business systems |
US9305548B2 (en) | 2008-05-27 | 2016-04-05 | Voicebox Technologies Corporation | System and method for an integrated, multi-modal, multi-device natural language voice services environment |
US8554767B2 (en) | 2008-12-23 | 2013-10-08 | Samsung Electronics Co., Ltd | Context-based interests in computing environments and systems |
US20090307053A1 (en) * | 2008-06-06 | 2009-12-10 | Ryan Steelberg | Apparatus, system and method for a brand affinity engine using positive and negative mentions |
CA2727711A1 (fr) * | 2008-06-12 | 2009-12-17 | Ryan Steelberg | Publicite munie d'un code a barres |
US20100036906A1 (en) * | 2008-08-05 | 2010-02-11 | Google Inc. | Advertisements for streaming media |
AU2009296763A1 (en) * | 2008-09-26 | 2010-04-01 | Brand Affinity Technologies, Inc. | An advertising request and rules-based content provision engine, system and method |
WO2010036643A1 (fr) * | 2008-09-26 | 2010-04-01 | Brand Affinity Technologies, Inc. | Demande de publicité et moteur, système et procédé de fourniture de contenu à base de règles |
WO2010039860A1 (fr) * | 2008-09-30 | 2010-04-08 | Brand Affinity Technologies, Inc. | Système et procédé de distribution et de placement de contenu par affinité de marque |
WO2010044868A1 (fr) * | 2008-10-14 | 2010-04-22 | Brand Affinity Technologies Inc. | Appareil, système et procédé pour un moteur d'affinité avec une marque utilisant des mentions positives et négatives et une indexation |
WO2010056545A1 (fr) * | 2008-10-29 | 2010-05-20 | Brand Affinity Technologies, Inc. | Système et procédé de création de métriques d'actifs dans une distribution de contenu par affinité à une marque |
WO2010054234A1 (fr) * | 2008-11-06 | 2010-05-14 | Brand Affinity Technologies, Inc. | Système et procédé de développement de logiciel et d'applications internet |
US8458601B2 (en) * | 2008-12-04 | 2013-06-04 | International Business Machines Corporation | System and method for item inquiry and information presentation via standard communication paths |
US20100145784A1 (en) * | 2008-12-04 | 2010-06-10 | Doapp, Inc. | Method and system for time-and location-sensitive customer loyalty rewards program |
US20100198604A1 (en) * | 2009-01-30 | 2010-08-05 | Samsung Electronics Co., Ltd. | Generation of concept relations |
US8326637B2 (en) | 2009-02-20 | 2012-12-04 | Voicebox Technologies, Inc. | System and method for processing multi-modal device interactions in a natural language voice services environment |
US10104436B1 (en) * | 2009-02-23 | 2018-10-16 | Beachfront Media Llc | Automated video-preroll method and device |
US20100235235A1 (en) * | 2009-03-10 | 2010-09-16 | Microsoft Corporation | Endorsable entity presentation based upon parsed instant messages |
US9760906B1 (en) | 2009-03-19 | 2017-09-12 | Google Inc. | Sharing revenue associated with a content item |
US8600849B1 (en) | 2009-03-19 | 2013-12-03 | Google Inc. | Controlling content items |
US9170995B1 (en) * | 2009-03-19 | 2015-10-27 | Google Inc. | Identifying context of content items |
WO2010118129A1 (fr) * | 2009-04-07 | 2010-10-14 | Fuhu, Inc. | Dispositif et procédé de création, distribution, gestion et monétisation de gadgets logiciels utilisant des modèles |
US20100269069A1 (en) * | 2009-04-17 | 2010-10-21 | Nokia Corporation | Method and apparatus of associating and maintaining state information for applications |
US8571936B2 (en) * | 2009-06-04 | 2013-10-29 | Viacom International Inc. | Dynamic integration and non-linear presentation of advertising content and media content |
US20110119278A1 (en) * | 2009-08-28 | 2011-05-19 | Resonate Networks, Inc. | Method and apparatus for delivering targeted content to website visitors to promote products and brands |
US10475047B2 (en) * | 2009-08-28 | 2019-11-12 | Resonate Networks, Inc. | Method and apparatus for delivering targeted content to website visitors |
US20110078021A1 (en) * | 2009-09-30 | 2011-03-31 | John Tullis | Mobile Device Including Mobile Application Coordinating External Data |
US9595040B2 (en) | 2009-10-09 | 2017-03-14 | Viacom International Inc. | Integration of an advertising unit containing interactive residual areas and digital media content |
US8752083B2 (en) | 2009-11-05 | 2014-06-10 | Viacom International Inc. | Integration of an interactive advertising unit containing a fully functional virtual object and digital media content |
US20120215646A1 (en) | 2009-12-09 | 2012-08-23 | Viacom International, Inc. | Integration of a Wall-to-Wall Advertising Unit and Digital Media Content |
US9152708B1 (en) | 2009-12-14 | 2015-10-06 | Google Inc. | Target-video specific co-watched video clusters |
US9015595B2 (en) * | 2010-01-20 | 2015-04-21 | Yahoo! Inc. | Self-targeting local AD system |
US9106873B2 (en) * | 2010-04-01 | 2015-08-11 | Verizon Patent And Licensing Inc. | Methods and systems for providing enhanced content by way of a virtual channel |
KR101103402B1 (ko) * | 2010-05-31 | 2012-01-05 | 쏠스펙트럼(주) | 시간에 따라 동적인 광고가 반영되는 동영상 제공 시스템 |
US20120041834A1 (en) * | 2010-08-13 | 2012-02-16 | Mcrae Ii James Duncan | System and Method for Utilizing Media Content to Initiate Conversations between Businesses and Consumers |
US20130227394A1 (en) * | 2010-10-10 | 2013-08-29 | Victor Sazhin Group Ltd. | Method, system and computer program product for replacing banners with widgets |
US20120158490A1 (en) * | 2010-12-16 | 2012-06-21 | Yahoo! Inc. | Sponsored search auction mechanism for rich media advertising |
US20120166284A1 (en) * | 2010-12-22 | 2012-06-28 | Erick Tseng | Pricing Relevant Notifications Provided to a User Based on Location and Social Information |
US20120254150A1 (en) * | 2011-04-01 | 2012-10-04 | Yahoo! Inc | Dynamic arrangement of e-circulars in rais (rich ads in search) advertisements based on real time and past user activity |
US20120278162A1 (en) * | 2011-04-29 | 2012-11-01 | Microsoft Corporation | Conducting an auction of services responsive to positional selection |
EP2549399A1 (fr) | 2011-07-19 | 2013-01-23 | Koninklijke Philips Electronics N.V. | Evaluation d'activité de voie Wnt utilisant un modelage probabilistique d'expression de gène cible |
US20130156399A1 (en) * | 2011-12-20 | 2013-06-20 | Microsoft Corporation | Embedding content in rich media |
WO2013097239A1 (fr) | 2011-12-31 | 2013-07-04 | Thomson Licensing | Procédé et dispositif de présentation de contenu |
US10303754B1 (en) | 2012-05-30 | 2019-05-28 | Callidus Software, Inc. | Creation and display of dynamic content component |
US10929889B1 (en) * | 2012-08-31 | 2021-02-23 | Groupon, Inc. | Promotion offering system |
US20140365299A1 (en) | 2013-06-07 | 2014-12-11 | Open Tv, Inc. | System and method for providing advertising consistency |
US9940972B2 (en) * | 2013-08-15 | 2018-04-10 | Cellular South, Inc. | Video to data |
US10218954B2 (en) * | 2013-08-15 | 2019-02-26 | Cellular South, Inc. | Video to data |
JP6139426B2 (ja) * | 2014-02-04 | 2017-05-31 | ヤフー株式会社 | 広告配信装置、広告配信方法、及び広告配信プログラム |
US9898459B2 (en) | 2014-09-16 | 2018-02-20 | Voicebox Technologies Corporation | Integration of domain information into state transitions of a finite state transducer for natural language processing |
US9626703B2 (en) | 2014-09-16 | 2017-04-18 | Voicebox Technologies Corporation | Voice commerce |
CN107003999B (zh) | 2014-10-15 | 2020-08-21 | 声钰科技 | 对用户的在先自然语言输入的后续响应的系统和方法 |
US20160110767A1 (en) * | 2014-10-21 | 2016-04-21 | Yahoo!, Inc. | Coupon provider |
US10431214B2 (en) | 2014-11-26 | 2019-10-01 | Voicebox Technologies Corporation | System and method of determining a domain and/or an action related to a natural language input |
US10614799B2 (en) | 2014-11-26 | 2020-04-07 | Voicebox Technologies Corporation | System and method of providing intent predictions for an utterance prior to a system detection of an end of the utterance |
US20160294890A1 (en) | 2015-03-31 | 2016-10-06 | Facebook, Inc. | Multi-user media presentation system |
US10331784B2 (en) | 2016-07-29 | 2019-06-25 | Voicebox Technologies Corporation | System and method of disambiguating natural language processing requests |
CA3048901A1 (fr) * | 2016-12-30 | 2018-07-05 | Social Media Broadcaster, LLC | Plate-forme de distribution de contenus video a mecanismes de collecte de publicite et de recompense integres |
CN107172151B (zh) * | 2017-05-18 | 2020-08-07 | 百度在线网络技术(北京)有限公司 | 用于推送信息的方法和装置 |
US10671798B2 (en) * | 2018-02-01 | 2020-06-02 | Google Llc | Digital component backdrop rendering |
US10235999B1 (en) | 2018-06-05 | 2019-03-19 | Voicify, LLC | Voice application platform |
US10636425B2 (en) | 2018-06-05 | 2020-04-28 | Voicify, LLC | Voice application platform |
US11437029B2 (en) * | 2018-06-05 | 2022-09-06 | Voicify, LLC | Voice application platform |
US10803865B2 (en) | 2018-06-05 | 2020-10-13 | Voicify, LLC | Voice application platform |
CN109389429A (zh) * | 2018-09-29 | 2019-02-26 | 北京奇虎科技有限公司 | 一种富媒体广告的制作方法及装置 |
CN109089173B (zh) * | 2018-10-08 | 2020-12-15 | 四川长虹电器股份有限公司 | 一种检测智能电视终端广告投放的方法及系统 |
SG10202010247QA (en) * | 2019-10-18 | 2021-05-28 | Affle Int Pte Ltd | Method and system for enabling an interaction of a user with one or more advertisements within a podcast |
Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5617486A (en) * | 1993-09-30 | 1997-04-01 | Apple Computer, Inc. | Continuous reference adaptation in a pattern recognition system |
US5778340A (en) * | 1994-09-08 | 1998-07-07 | Nec Corporation | Adapting input speech and reference patterns for changing speaker and environment |
US5864810A (en) * | 1995-01-20 | 1999-01-26 | Sri International | Method and apparatus for speech recognition adapted to an individual speaker |
US6208720B1 (en) * | 1998-04-23 | 2001-03-27 | Mci Communications Corporation | System, method and computer program product for a dynamic rules-based threshold engine |
US6223159B1 (en) * | 1998-02-25 | 2001-04-24 | Mitsubishi Denki Kabushiki Kaisha | Speaker adaptation device and speech recognition device |
US6285999B1 (en) * | 1997-01-10 | 2001-09-04 | The Board Of Trustees Of The Leland Stanford Junior University | Method for node ranking in a linked database |
US6343267B1 (en) * | 1998-04-30 | 2002-01-29 | Matsushita Electric Industrial Co., Ltd. | Dimensionality reduction for speaker normalization and speaker and environment adaptation using eigenvoice techniques |
US6389377B1 (en) * | 1997-12-01 | 2002-05-14 | The Johns Hopkins University | Methods and apparatus for acoustic transient processing |
US20030220791A1 (en) * | 2002-04-26 | 2003-11-27 | Pioneer Corporation | Apparatus and method for speech recognition |
US6879956B1 (en) * | 1999-09-30 | 2005-04-12 | Sony Corporation | Speech recognition with feedback from natural language processing for adaptation of acoustic models |
US6907566B1 (en) * | 1999-04-02 | 2005-06-14 | Overture Services, Inc. | Method and system for optimum placement of advertisements on a webpage |
US20050182626A1 (en) * | 2004-02-18 | 2005-08-18 | Samsung Electronics Co., Ltd. | Speaker clustering and adaptation method based on the HMM model variation information and its apparatus for speech recognition |
US20050190973A1 (en) * | 2004-02-27 | 2005-09-01 | International Business Machines Corporation | System and method for recognizing word patterns in a very large vocabulary based on a virtual keyboard layout |
US20050192802A1 (en) * | 2004-02-11 | 2005-09-01 | Alex Robinson | Handwriting and voice input with automatic correction |
US20060058999A1 (en) * | 2004-09-10 | 2006-03-16 | Simon Barker | Voice model adaptation |
US7065488B2 (en) * | 2000-09-29 | 2006-06-20 | Pioneer Corporation | Speech recognition system with an adaptive acoustic model |
Family Cites Families (131)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6553178B2 (en) * | 1992-02-07 | 2003-04-22 | Max Abecassis | Advertisement subsidized video-on-demand system |
US5933811A (en) | 1996-08-20 | 1999-08-03 | Paul D. Angles | System and method for delivering customized advertisements within interactive communication systems |
US5951642A (en) | 1997-08-06 | 1999-09-14 | Hypertak, Inc. | System for collecting detailed internet information on the basis of the condition of activities of information viewers viewing information of service providers |
US6134532A (en) * | 1997-11-14 | 2000-10-17 | Aptex Software, Inc. | System and method for optimal adaptive matching of users to most relevant entity and information in real-time |
US20030061566A1 (en) | 1998-10-30 | 2003-03-27 | Rubstein Laila J. | Dynamic integration of digital files for transmission over a network and file usage control |
US6560578B2 (en) * | 1999-03-12 | 2003-05-06 | Expanse Networks, Inc. | Advertisement selection system supporting discretionary target market characteristics |
US6704930B1 (en) | 1999-04-20 | 2004-03-09 | Expanse Networks, Inc. | Advertisement insertion techniques for digital video streams |
US11109114B2 (en) | 2001-04-18 | 2021-08-31 | Grass Valley Canada | Advertisement management method, system, and computer program product |
US6202087B1 (en) | 1999-03-22 | 2001-03-13 | Ofer Gadish | Replacement of error messages with non-error messages |
WO2001020908A1 (fr) | 1999-09-16 | 2001-03-22 | Ixl Enterprises, Inc. | Systeme et procede de liaison de contenu mediatique |
US20050076357A1 (en) | 1999-10-28 | 2005-04-07 | Fenne Adam Michael | Dynamic insertion of targeted sponsored video messages into Internet multimedia broadcasts |
US7822636B1 (en) * | 1999-11-08 | 2010-10-26 | Aol Advertising, Inc. | Optimal internet ad placement |
AU1354901A (en) * | 1999-11-10 | 2001-06-06 | Amazon.Com, Inc. | Method and system for allocating display space |
JP3476185B2 (ja) | 1999-12-27 | 2003-12-10 | インターナショナル・ビジネス・マシーンズ・コーポレーション | 情報抽出システム、情報処理装置、情報収集装置、文字列抽出方法及び記憶媒体 |
US20010049824A1 (en) | 2000-01-25 | 2001-12-06 | Baker Stanley C. | Internet business model for the production, market making and distribution of audio and multimedia programs |
US6505169B1 (en) | 2000-01-26 | 2003-01-07 | At&T Corp. | Method for adaptive ad insertion in streaming multimedia content |
US6636247B1 (en) | 2000-01-31 | 2003-10-21 | International Business Machines Corporation | Modality advertisement viewing system and method |
US20010042249A1 (en) | 2000-03-15 | 2001-11-15 | Dan Knepper | System and method of joining encoded video streams for continuous play |
US8171509B1 (en) * | 2000-04-07 | 2012-05-01 | Virage, Inc. | System and method for applying a database to video multimedia |
US20020032904A1 (en) | 2000-05-24 | 2002-03-14 | Lerner David S. | Interactive system and method for collecting data and generating reports regarding viewer habits |
US20050210145A1 (en) | 2000-07-24 | 2005-09-22 | Vivcom, Inc. | Delivering and processing multimedia bookmark |
US6944585B1 (en) * | 2000-09-01 | 2005-09-13 | Oracle International Corporation | Dynamic personalized content resolution for a media server |
US20020049635A1 (en) * | 2000-09-06 | 2002-04-25 | Khanh Mai | Multiple advertising |
US6950623B2 (en) | 2000-09-19 | 2005-09-27 | Loudeye Corporation | Methods and systems for dynamically serving in-stream advertisements |
US20020082941A1 (en) | 2000-10-16 | 2002-06-27 | Bird Benjamin David Arthur | Method and system for the dynamic delivery, presentation, organization, storage, and retrieval of content and third party advertising information via a network |
JP2004518202A (ja) | 2000-10-24 | 2004-06-17 | トムソン ライセンシング ソシエテ アノニム | 埋め込み型メディア・プレーヤ・ページを使用して広告を配信する方法、記録媒体、および伝送媒体 |
US6952419B1 (en) | 2000-10-25 | 2005-10-04 | Sun Microsystems, Inc. | High performance transmission link and interconnect |
US20020174425A1 (en) | 2000-10-26 | 2002-11-21 | Markel Steven O. | Collection of affinity data from television, video, or similar transmissions |
US7331057B2 (en) * | 2000-12-28 | 2008-02-12 | Prime Research Alliance E, Inc. | Grouping advertisement subavails |
US6839865B2 (en) | 2000-12-29 | 2005-01-04 | Road Runner | System and method for multicast stream failover |
US20070300258A1 (en) | 2001-01-29 | 2007-12-27 | O'connor Daniel | Methods and systems for providing media assets over a network |
US6925649B2 (en) * | 2001-03-30 | 2005-08-02 | Sharp Laboratories Of America, Inc. | Methods and systems for mass customization of digital television broadcasts in DASE environments |
US20020154163A1 (en) | 2001-04-18 | 2002-10-24 | Oak Interactive Ltd. | Advertising system for interactive multi-stages advertisements that use the non-used areas of the browser interface |
EP1276061A1 (fr) * | 2001-07-09 | 2003-01-15 | Accenture | Procédé et système informatique pour la détermination de l'index de satisfaction d'un texte |
US7007074B2 (en) | 2001-09-10 | 2006-02-28 | Yahoo! Inc. | Targeted advertisements using time-dependent key search terms |
US7117439B2 (en) | 2001-10-19 | 2006-10-03 | Microsoft Corporation | Advertising using a combination of video and banner advertisements |
US20030079226A1 (en) | 2001-10-19 | 2003-04-24 | Barrett Peter T. | Video segment targeting using remotely issued instructions and localized state and behavior information |
US7136871B2 (en) | 2001-11-21 | 2006-11-14 | Microsoft Corporation | Methods and systems for selectively displaying advertisements |
US7610358B2 (en) | 2001-11-26 | 2009-10-27 | Time Warner Cable | System and method for effectively presenting multimedia information materials |
US7813954B1 (en) | 2001-12-14 | 2010-10-12 | Keen Personal Media, Inc. | Audiovisual system and method for displaying segmented advertisements tailored to the characteristic viewing preferences of a user |
US7064796B2 (en) * | 2001-12-21 | 2006-06-20 | Eloda Inc. | Method and system for re-identifying broadcast segments using statistical profiles |
US7765567B2 (en) | 2002-01-02 | 2010-07-27 | Sony Corporation | Content replacement by PID mapping |
US20050114198A1 (en) | 2003-11-24 | 2005-05-26 | Ross Koningstein | Using concepts for ad targeting |
US7716161B2 (en) | 2002-09-24 | 2010-05-11 | Google, Inc, | Methods and apparatus for serving relevant advertisements |
US7136875B2 (en) * | 2002-09-24 | 2006-11-14 | Google, Inc. | Serving advertisements based on content |
US20030204844A1 (en) * | 2002-04-26 | 2003-10-30 | Brant Steven B. | Video messaging system |
US20040001081A1 (en) | 2002-06-19 | 2004-01-01 | Marsh David J. | Methods and systems for enhancing electronic program guides |
US20040003397A1 (en) | 2002-06-27 | 2004-01-01 | International Business Machines Corporation | System and method for customized video commercial distribution |
US8090798B2 (en) * | 2002-08-12 | 2012-01-03 | Morganstein | System and methods for direct targeted media advertising over peer-to-peer networks |
WO2004017178A2 (fr) | 2002-08-19 | 2004-02-26 | Choicestream | Systeme de recommandation statistique personnalise |
EP1860579A1 (fr) * | 2002-08-30 | 2007-11-28 | Sony Deutschland Gmbh | Procédé de division d'un profile multiutilisateur |
US20040059712A1 (en) * | 2002-09-24 | 2004-03-25 | Dean Jeffrey A. | Serving advertisements using information associated with e-mail |
KR100393821B1 (en) | 2002-12-09 | 2003-08-02 | Bong Chun Jeung | System for managing non-certification connection of cooperated site linking with ad |
US20040204983A1 (en) | 2003-04-10 | 2004-10-14 | David Shen | Method and apparatus for assessment of effectiveness of advertisements on an Internet hub network |
US7363302B2 (en) * | 2003-06-30 | 2008-04-22 | Googole, Inc. | Promoting and/or demoting an advertisement from an advertising spot of one type to an advertising spot of another type |
US20050102375A1 (en) | 2003-10-23 | 2005-05-12 | Kivin Varghese | An Internet System for the Uploading, Viewing and Rating of Videos |
US20050149396A1 (en) | 2003-11-21 | 2005-07-07 | Marchex, Inc. | Online advertising system and method |
US7979877B2 (en) * | 2003-12-23 | 2011-07-12 | Intellocity Usa Inc. | Advertising methods for advertising time slots and embedded objects |
SG119229A1 (en) | 2004-07-30 | 2006-02-28 | Agency Science Tech & Res | Method and apparatus for insertion of additional content into video |
US8135803B2 (en) | 2004-08-23 | 2012-03-13 | Ianywhere Solutions, Inc. | Method, system, and computer program product for offline advertisement servicing and cycling |
US20060063587A1 (en) * | 2004-09-13 | 2006-03-23 | Manzo Anthony V | Gaming advertisement systems and methods |
US20060074753A1 (en) * | 2004-10-06 | 2006-04-06 | Kimberly-Clark Worldwide, Inc. | Advertising during printing of secure customized coupons |
US20060080171A1 (en) | 2004-10-08 | 2006-04-13 | Jardins G T D | Managing advertising inventory |
US20080170110A1 (en) | 2004-11-17 | 2008-07-17 | Nu-Kote International, Inc. | Circuit board with terminals arranged in a single row and disposed at board edges, cartridges with the circuit board, and methods for making same |
US20060135232A1 (en) * | 2004-12-17 | 2006-06-22 | Daniel Willis | Method and system for delivering advertising content to video games based on game events and gamer activity |
US20060287915A1 (en) * | 2005-01-12 | 2006-12-21 | Boulet Daniel A | Scheduling content insertion opportunities in a broadcast network |
EP1846884A4 (fr) | 2005-01-14 | 2010-02-17 | Tremor Media Llc | Systeme et procede de publicite dynamique |
US20060161553A1 (en) * | 2005-01-19 | 2006-07-20 | Tiny Engine, Inc. | Systems and methods for providing user interaction based profiles |
US8001005B2 (en) | 2005-01-25 | 2011-08-16 | Moreover Acquisition Corporation | Systems and methods for providing advertising in a feed of content |
US8768766B2 (en) * | 2005-03-07 | 2014-07-01 | Turn Inc. | Enhanced online advertising system |
WO2006099583A2 (fr) * | 2005-03-16 | 2006-09-21 | 121 Media, Inc. | Systeme et procede de publicite ciblee |
US20060212897A1 (en) * | 2005-03-18 | 2006-09-21 | Microsoft Corporation | System and method for utilizing the content of audio/video files to select advertising content for display |
US20060224444A1 (en) * | 2005-03-30 | 2006-10-05 | Ross Koningstein | Networking advertisers and agents for ad authoring and/or ad campaign management |
US8924256B2 (en) * | 2005-03-31 | 2014-12-30 | Google Inc. | System and method for obtaining content based on data from an electronic device |
US7653627B2 (en) * | 2005-05-13 | 2010-01-26 | Microsoft Corporation | System and method for utilizing the content of an online conversation to select advertising content and/or other relevant information for display |
US8145528B2 (en) * | 2005-05-23 | 2012-03-27 | Open Text S.A. | Movie advertising placement optimization based on behavior and content analysis |
US7356590B2 (en) | 2005-07-12 | 2008-04-08 | Visible Measures Corp. | Distributed capture and aggregation of dynamic application usage information |
US7991764B2 (en) | 2005-07-22 | 2011-08-02 | Yogesh Chunilal Rathod | Method and system for communication, publishing, searching, sharing and dynamically providing a journal feed |
US8326689B2 (en) * | 2005-09-16 | 2012-12-04 | Google Inc. | Flexible advertising system which allows advertisers with different value propositions to express such value propositions to the advertising system |
US8370197B2 (en) | 2005-09-30 | 2013-02-05 | Google Inc. | Controlling the serving of advertisements, such as cost per impression advertisements for example, to improve the value of such serves |
US20070094363A1 (en) | 2005-10-25 | 2007-04-26 | Podbridge, Inc. | Configuration for ad and content delivery in time and space shifted media network |
US8180826B2 (en) | 2005-10-31 | 2012-05-15 | Microsoft Corporation | Media sharing and authoring on the web |
US20070112630A1 (en) | 2005-11-07 | 2007-05-17 | Scanscout, Inc. | Techniques for rendering advertisments with rich media |
GB2435114A (en) | 2006-02-08 | 2007-08-15 | Rapid Mobile Media Ltd | Providing targeted additional content |
US7792815B2 (en) | 2006-03-06 | 2010-09-07 | Veveo, Inc. | Methods and systems for selecting and presenting content based on context sensitive user preferences |
US8495204B2 (en) | 2006-07-06 | 2013-07-23 | Visible Measures Corp. | Remote invocation mechanism for logging |
US20080045336A1 (en) | 2006-08-18 | 2008-02-21 | Merit Industries, Inc. | Interactive amusement device advertising |
US20080046562A1 (en) | 2006-08-21 | 2008-02-21 | Crazy Egg, Inc. | Visual web page analytics |
US8688522B2 (en) | 2006-09-06 | 2014-04-01 | Mediamath, Inc. | System and method for dynamic online advertisement creation and management |
US20080066107A1 (en) | 2006-09-12 | 2008-03-13 | Google Inc. | Using Viewing Signals in Targeted Video Advertising |
US20080082402A1 (en) | 2006-09-19 | 2008-04-03 | Paranormalresearch.Com | Advertisement server for wireless access points |
US8370732B2 (en) | 2006-10-20 | 2013-02-05 | Mixpo Portfolio Broadcasting, Inc. | Peer-to-portal media broadcasting |
EP2087425B1 (fr) | 2006-10-25 | 2019-07-24 | Dynatrace LLC | Procédés et dispositif pour surveiller la génération de page web |
US20080288973A1 (en) | 2007-05-18 | 2008-11-20 | Carson David V | System and Method for Providing Advertisements for Video Content in a Packet Based Network |
US20080109300A1 (en) | 2006-11-06 | 2008-05-08 | Bason Brian J | System and Method for Managing the Distribution of Advertisements for Video Content |
US20080109391A1 (en) * | 2006-11-07 | 2008-05-08 | Scanscout, Inc. | Classifying content based on mood |
US20080133475A1 (en) | 2006-11-30 | 2008-06-05 | Donald Fischer | Identification of interesting content based on observation of passive user interaction |
US20080178230A1 (en) | 2006-12-05 | 2008-07-24 | Crackle, Inc. | Video sharing platform providing for public and private sharing and distributed downloads of videos |
US20080183555A1 (en) | 2007-01-29 | 2008-07-31 | Hunter Walk | Determining and communicating excess advertiser demand information to users, such as publishers participating in, or expected to participate in, an advertising network |
US20080228576A1 (en) * | 2007-03-13 | 2008-09-18 | Scanscout, Inc. | Ad performance optimization for rich media content |
US20080228581A1 (en) * | 2007-03-13 | 2008-09-18 | Tadashi Yonezaki | Method and System for a Natural Transition Between Advertisements Associated with Rich Media Content |
WO2008124033A2 (fr) | 2007-04-03 | 2008-10-16 | Grape Technology Group Inc. | Système et procédé pour moteur de recherche personnalisé et optimisation du résultat de recherche |
US20080300989A1 (en) | 2007-05-31 | 2008-12-04 | Eyewonder, Inc. | Systems and methods for generating, reviewing, editing, and transmitting an advertising unit in a single environment |
US8307392B2 (en) | 2007-06-11 | 2012-11-06 | Yahoo! Inc. | Systems and methods for inserting ads during playback of video media |
US20080319850A1 (en) | 2007-06-20 | 2008-12-25 | Sekindo Ltd | Method for managing website advertising space |
US20080319827A1 (en) | 2007-06-25 | 2008-12-25 | Microsoft Corporation | Mining implicit behavior |
US20080320531A1 (en) | 2007-06-25 | 2008-12-25 | Interpols Network Incorporated | Systems and methods for third-party aggregated video ratings |
US9654721B2 (en) | 2007-07-10 | 2017-05-16 | Verizon Patent And Licensing Inc. | System and method for providing personal content recommendations |
US8577996B2 (en) | 2007-09-18 | 2013-11-05 | Tremor Video, Inc. | Method and apparatus for tracing users of online video web sites |
US8549550B2 (en) | 2008-09-17 | 2013-10-01 | Tubemogul, Inc. | Method and apparatus for passively monitoring online video viewing and viewer behavior |
US20090119169A1 (en) | 2007-10-02 | 2009-05-07 | Blinkx Uk Ltd | Various methods and apparatuses for an engine that pairs advertisements with video files |
US20090089830A1 (en) | 2007-10-02 | 2009-04-02 | Blinkx Uk Ltd | Various methods and apparatuses for pairing advertisements with video files |
US8640030B2 (en) | 2007-10-07 | 2014-01-28 | Fall Front Wireless Ny, Llc | User interface for creating tags synchronized with a video playback |
US20090132355A1 (en) * | 2007-11-19 | 2009-05-21 | Att Knowledge Ventures L.P. | System and method for automatically selecting advertising for video data |
US20090171728A1 (en) | 2007-12-27 | 2009-07-02 | Yan Tak W | Simulation framework for evaluating designs for sponsored search markets |
US20090172727A1 (en) * | 2007-12-28 | 2009-07-02 | Google Inc. | Selecting advertisements to present |
US20090187480A1 (en) | 2008-01-22 | 2009-07-23 | Tellabs Vienna, Inc. | Method, system, apparatus, and computer program for providing selective advertising to subscribers |
US20090259551A1 (en) | 2008-04-11 | 2009-10-15 | Tremor Media, Inc. | System and method for inserting advertisements from multiple ad servers via a master component |
US8301497B2 (en) | 2008-04-17 | 2012-10-30 | Aol Advertising Inc. | Method and system for media initialization via data sharing |
WO2009158581A2 (fr) * | 2008-06-27 | 2009-12-30 | Adpassage, Inc. | Système et procédé de reconnaissance de sujet parlé ou de critère dans un contenu numérique et de la publicité contextuelle |
US20100011020A1 (en) | 2008-07-11 | 2010-01-14 | Motorola, Inc. | Recommender system |
US20100023960A1 (en) | 2008-07-22 | 2010-01-28 | General Instrument Corporation | Detection of Video Program Viewing Behavior for Correlation with Advertisement Presentation |
US20100057576A1 (en) | 2008-09-02 | 2010-03-04 | Apple Inc. | System and method for video insertion into media stream or file |
US9612995B2 (en) | 2008-09-17 | 2017-04-04 | Adobe Systems Incorporated | Video viewer targeting based on preference similarity |
US20100114696A1 (en) | 2008-10-31 | 2010-05-06 | Yahoo! Inc. | Method of programmed allocation of advertising opportunities for conformance with goals |
US20100121776A1 (en) | 2008-11-07 | 2010-05-13 | Peter Stenger | Performance monitoring system |
KR101792587B1 (ko) | 2009-01-23 | 2017-11-02 | 삼성전자주식회사 | 컨텐츠에 대한 선호도 예측 방법 및 장치와, 샘플 컨텐츠 선정 방법 및 장치 |
US8065199B2 (en) | 2009-04-08 | 2011-11-22 | Ebay Inc. | Method, medium, and system for adjusting product ranking scores based on an adjustment factor |
US20110093783A1 (en) | 2009-10-16 | 2011-04-21 | Charles Parra | Method and system for linking media components |
CA2781299A1 (fr) | 2009-11-20 | 2012-05-03 | Tadashi Yonezaki | Procedes et appareil d'optimisation d'allocation de publicite |
US20120203598A1 (en) | 2011-02-09 | 2012-08-09 | VisionEdge Marketing | File Server System and Method of Providing a Marketing Performance and Accountability Audit |
-
2006
- 2006-11-07 US US11/594,707 patent/US20070112630A1/en not_active Abandoned
- 2006-11-07 US US11/594,717 patent/US20070112567A1/en not_active Abandoned
- 2006-11-07 JP JP2008539128A patent/JP2009521736A/ja active Pending
- 2006-11-07 EP EP06837147A patent/EP1952326A4/fr not_active Withdrawn
- 2006-11-07 WO PCT/US2006/043475 patent/WO2007056451A2/fr active Application Filing
- 2006-11-07 WO PCT/US2006/043292 patent/WO2007056344A2/fr active Application Filing
-
2012
- 2012-02-29 US US13/408,459 patent/US9563826B2/en active Active
-
2017
- 2017-02-03 US US15/424,257 patent/US20170364777A1/en not_active Abandoned
Patent Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5617486A (en) * | 1993-09-30 | 1997-04-01 | Apple Computer, Inc. | Continuous reference adaptation in a pattern recognition system |
US5778340A (en) * | 1994-09-08 | 1998-07-07 | Nec Corporation | Adapting input speech and reference patterns for changing speaker and environment |
US5864810A (en) * | 1995-01-20 | 1999-01-26 | Sri International | Method and apparatus for speech recognition adapted to an individual speaker |
US6285999B1 (en) * | 1997-01-10 | 2001-09-04 | The Board Of Trustees Of The Leland Stanford Junior University | Method for node ranking in a linked database |
US6389377B1 (en) * | 1997-12-01 | 2002-05-14 | The Johns Hopkins University | Methods and apparatus for acoustic transient processing |
US6223159B1 (en) * | 1998-02-25 | 2001-04-24 | Mitsubishi Denki Kabushiki Kaisha | Speaker adaptation device and speech recognition device |
US6208720B1 (en) * | 1998-04-23 | 2001-03-27 | Mci Communications Corporation | System, method and computer program product for a dynamic rules-based threshold engine |
US6343267B1 (en) * | 1998-04-30 | 2002-01-29 | Matsushita Electric Industrial Co., Ltd. | Dimensionality reduction for speaker normalization and speaker and environment adaptation using eigenvoice techniques |
US6907566B1 (en) * | 1999-04-02 | 2005-06-14 | Overture Services, Inc. | Method and system for optimum placement of advertisements on a webpage |
US6879956B1 (en) * | 1999-09-30 | 2005-04-12 | Sony Corporation | Speech recognition with feedback from natural language processing for adaptation of acoustic models |
US7065488B2 (en) * | 2000-09-29 | 2006-06-20 | Pioneer Corporation | Speech recognition system with an adaptive acoustic model |
US20030220791A1 (en) * | 2002-04-26 | 2003-11-27 | Pioneer Corporation | Apparatus and method for speech recognition |
US20050192802A1 (en) * | 2004-02-11 | 2005-09-01 | Alex Robinson | Handwriting and voice input with automatic correction |
US20050182626A1 (en) * | 2004-02-18 | 2005-08-18 | Samsung Electronics Co., Ltd. | Speaker clustering and adaptation method based on the HMM model variation information and its apparatus for speech recognition |
US20050190973A1 (en) * | 2004-02-27 | 2005-09-01 | International Business Machines Corporation | System and method for recognizing word patterns in a very large vocabulary based on a virtual keyboard layout |
US20060058999A1 (en) * | 2004-09-10 | 2006-03-16 | Simon Barker | Voice model adaptation |
Cited By (38)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20060242016A1 (en) * | 2005-01-14 | 2006-10-26 | Tremor Media Llc | Dynamic advertisement system and method |
US20080270129A1 (en) * | 2005-02-17 | 2008-10-30 | Loquendo S.P.A. | Method and System for Automatically Providing Linguistic Formulations that are Outside a Recognition Domain of an Automatic Speech Recognition System |
US9224391B2 (en) * | 2005-02-17 | 2015-12-29 | Nuance Communications, Inc. | Method and system for automatically providing linguistic formulations that are outside a recognition domain of an automatic speech recognition system |
US20070112630A1 (en) * | 2005-11-07 | 2007-05-17 | Scanscout, Inc. | Techniques for rendering advertisments with rich media |
US9563826B2 (en) | 2005-11-07 | 2017-02-07 | Tremor Video, Inc. | Techniques for rendering advertisements with rich media |
US20080109391A1 (en) * | 2006-11-07 | 2008-05-08 | Scanscout, Inc. | Classifying content based on mood |
US10270870B2 (en) | 2007-09-18 | 2019-04-23 | Adobe Inc. | Passively monitoring online video viewing and viewer behavior |
US20090083417A1 (en) * | 2007-09-18 | 2009-03-26 | John Hughes | Method and apparatus for tracing users of online video web sites |
US8577996B2 (en) | 2007-09-18 | 2013-11-05 | Tremor Video, Inc. | Method and apparatus for tracing users of online video web sites |
US20090138332A1 (en) * | 2007-11-23 | 2009-05-28 | Dimitri Kanevsky | System and method for dynamically adapting a user slide show presentation to audience behavior |
US20090204243A1 (en) * | 2008-01-09 | 2009-08-13 | 8 Figure, Llc | Method and apparatus for creating customized text-to-speech podcasts and videos incorporating associated media |
US20090259552A1 (en) * | 2008-04-11 | 2009-10-15 | Tremor Media, Inc. | System and method for providing advertisements from multiple ad servers using a failover mechanism |
US10462504B2 (en) | 2008-09-17 | 2019-10-29 | Adobe Inc. | Targeting videos based on viewer similarity |
US8549550B2 (en) | 2008-09-17 | 2013-10-01 | Tubemogul, Inc. | Method and apparatus for passively monitoring online video viewing and viewer behavior |
US9967603B2 (en) | 2008-09-17 | 2018-05-08 | Adobe Systems Incorporated | Video viewer targeting based on preference similarity |
US9781221B2 (en) | 2008-09-17 | 2017-10-03 | Adobe Systems Incorporated | Method and apparatus for passively monitoring online video viewing and viewer behavior |
US9612995B2 (en) | 2008-09-17 | 2017-04-04 | Adobe Systems Incorporated | Video viewer targeting based on preference similarity |
US9485316B2 (en) | 2008-09-17 | 2016-11-01 | Tubemogul, Inc. | Method and apparatus for passively monitoring online video viewing and viewer behavior |
US20110029666A1 (en) * | 2008-09-17 | 2011-02-03 | Lopatecki Jason | Method and Apparatus for Passively Monitoring Online Video Viewing and Viewer Behavior |
US20110202411A1 (en) * | 2008-11-04 | 2011-08-18 | Kentaro Nakai | Advertising voice control device, integrated circuit, advertising voice control method, advertising voice control program, and recording medium |
US20110093783A1 (en) * | 2009-10-16 | 2011-04-21 | Charles Parra | Method and system for linking media components |
US8615430B2 (en) | 2009-11-20 | 2013-12-24 | Tremor Video, Inc. | Methods and apparatus for optimizing advertisement allocation |
US20110125573A1 (en) * | 2009-11-20 | 2011-05-26 | Scanscout, Inc. | Methods and apparatus for optimizing advertisement allocation |
US20120245934A1 (en) * | 2011-03-25 | 2012-09-27 | General Motors Llc | Speech recognition dependent on text message content |
US9202465B2 (en) * | 2011-03-25 | 2015-12-01 | General Motors Llc | Speech recognition dependent on text message content |
US9595258B2 (en) | 2011-04-04 | 2017-03-14 | Digimarc Corporation | Context-based smartphone sensor logic |
US10930289B2 (en) | 2011-04-04 | 2021-02-23 | Digimarc Corporation | Context-based smartphone sensor logic |
US10510349B2 (en) | 2011-04-04 | 2019-12-17 | Digimarc Corporation | Context-based smartphone sensor logic |
US10199042B2 (en) | 2011-04-04 | 2019-02-05 | Digimarc Corporation | Context-based smartphone sensor logic |
US9953638B2 (en) * | 2012-06-28 | 2018-04-24 | Nuance Communications, Inc. | Meta-data inputs to front end processing for automatic speech recognition |
US20150262575A1 (en) * | 2012-06-28 | 2015-09-17 | Nuance Communications, Inc. | Meta-data inputs to front end processing for automatic speech recognition |
US11049094B2 (en) | 2014-02-11 | 2021-06-29 | Digimarc Corporation | Methods and arrangements for device to device communication |
US20160189730A1 (en) * | 2014-12-30 | 2016-06-30 | Iflytek Co., Ltd. | Speech separation method and system |
CN110088833A (zh) * | 2016-12-19 | 2019-08-02 | 三星电子株式会社 | 语音识别方法和装置 |
WO2018117532A1 (fr) * | 2016-12-19 | 2018-06-28 | Samsung Electronics Co., Ltd. | Procédé et appareil de reconnaissance vocale |
US10770065B2 (en) | 2016-12-19 | 2020-09-08 | Samsung Electronics Co., Ltd. | Speech recognition method and apparatus |
KR102691541B1 (ko) | 2016-12-19 | 2024-08-02 | 삼성전자주식회사 | 음성 인식 방법 및 장치 |
US20220318853A1 (en) * | 2019-05-08 | 2022-10-06 | Data Vault Holdings, Inc. | System and method for tokenized licensing of content |
Also Published As
Publication number | Publication date |
---|---|
US9563826B2 (en) | 2017-02-07 |
US20170364777A1 (en) | 2017-12-21 |
WO2007056451A3 (fr) | 2009-05-07 |
WO2007056344A3 (fr) | 2007-12-21 |
JP2009521736A (ja) | 2009-06-04 |
EP1952326A2 (fr) | 2008-08-06 |
US20070112630A1 (en) | 2007-05-17 |
WO2007056344A2 (fr) | 2007-05-18 |
US20120278169A1 (en) | 2012-11-01 |
WO2007056451A2 (fr) | 2007-05-18 |
EP1952326A4 (fr) | 2010-08-04 |
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