[go: up one dir, main page]

US20070112567A1 - Techiques for model optimization for statistical pattern recognition - Google Patents

Techiques for model optimization for statistical pattern recognition Download PDF

Info

Publication number
US20070112567A1
US20070112567A1 US11/594,717 US59471706A US2007112567A1 US 20070112567 A1 US20070112567 A1 US 20070112567A1 US 59471706 A US59471706 A US 59471706A US 2007112567 A1 US2007112567 A1 US 2007112567A1
Authority
US
United States
Prior art keywords
model
content
pattern recognition
statistical pattern
models
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Abandoned
Application number
US11/594,717
Other languages
English (en)
Inventor
Wai Lau
Steven Lee
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
ScanScout Inc
Original Assignee
ScanScout Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ScanScout Inc filed Critical ScanScout Inc
Priority to US11/594,717 priority Critical patent/US20070112567A1/en
Assigned to SCANSCOUT, INC. reassignment SCANSCOUT, INC. ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LAU, WAI KIT, LEE, STEVEN
Publication of US20070112567A1 publication Critical patent/US20070112567A1/en
Abandoned legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0247—Calculate past, present or future revenues
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0252—Targeted advertisements based on events or environment, e.g. weather or festivals
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0255—Targeted advertisements based on user history
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0261—Targeted advertisements based on user location
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0264—Targeted advertisements based upon schedule
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0241—Advertisements
    • G06Q30/0273—Determination of fees for advertising
    • G06Q30/0275—Auctions
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/768—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using context analysis, e.g. recognition aided by known co-occurring patterns
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice

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.

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Finance (AREA)
  • Development Economics (AREA)
  • Accounting & Taxation (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computational Linguistics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Environmental & Geological Engineering (AREA)
  • Acoustics & Sound (AREA)
  • Human Computer Interaction (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Software Systems (AREA)
  • Evolutionary Computation (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
  • Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
  • Machine Translation (AREA)
  • Image Analysis (AREA)
US11/594,717 2005-11-07 2006-11-07 Techiques for model optimization for statistical pattern recognition Abandoned US20070112567A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US11/594,717 US20070112567A1 (en) 2005-11-07 2006-11-07 Techiques for model optimization for statistical pattern recognition

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US73387405P 2005-11-07 2005-11-07
US78441506P 2006-03-20 2006-03-20
US11/594,717 US20070112567A1 (en) 2005-11-07 2006-11-07 Techiques for model optimization for statistical pattern recognition

Publications (1)

Publication Number Publication Date
US20070112567A1 true US20070112567A1 (en) 2007-05-17

Family

ID=38023917

Family Applications (4)

Application Number Title Priority Date Filing Date
US11/594,707 Abandoned US20070112630A1 (en) 2005-11-07 2006-11-07 Techniques for rendering advertisments with rich media
US11/594,717 Abandoned US20070112567A1 (en) 2005-11-07 2006-11-07 Techiques for model optimization for statistical pattern recognition
US13/408,459 Active 2027-03-18 US9563826B2 (en) 2005-11-07 2012-02-29 Techniques for rendering advertisements with rich media
US15/424,257 Abandoned US20170364777A1 (en) 2005-11-07 2017-02-03 Techniques for rendering advertisements with rich media

Family Applications Before (1)

Application Number Title Priority Date Filing Date
US11/594,707 Abandoned US20070112630A1 (en) 2005-11-07 2006-11-07 Techniques for rendering advertisments with rich media

Family Applications After (2)

Application Number Title Priority Date Filing Date
US13/408,459 Active 2027-03-18 US9563826B2 (en) 2005-11-07 2012-02-29 Techniques for rendering advertisements with rich media
US15/424,257 Abandoned US20170364777A1 (en) 2005-11-07 2017-02-03 Techniques for rendering advertisements with rich media

Country Status (4)

Country Link
US (4) US20070112630A1 (fr)
EP (1) EP1952326A4 (fr)
JP (1) JP2009521736A (fr)
WO (2) WO2007056451A2 (fr)

Cited By (21)

* Cited by examiner, † Cited by third party
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
KR20180070970A (ko) * 2016-12-19 2018-06-27 삼성전자주식회사 음성 인식 방법 및 장치
US20210103825A1 (en) * 2016-04-08 2021-04-08 International Business Machines Corporation Generating a solution for an optimization problem
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 (153)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1279164A1 (fr) * 2000-04-28 2003-01-29 Deutsche Telekom AG Procede de calcul d'une decision d'activite vocale (detecteur d'activite vocale)
US20090228354A1 (en) * 2008-03-05 2009-09-10 Ryan Steelberg Engine, system and method for generation of brand affinity content
US20090112715A1 (en) * 2007-10-31 2009-04-30 Ryan Steelberg Engine, system and method for generation of brand affinity content
US20090018922A1 (en) * 2002-02-06 2009-01-15 Ryan Steelberg System and method for preemptive brand affinity content distribution
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
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
JP2009520411A (ja) * 2005-12-15 2009-05-21 グーグル・インコーポレーテッド コンテンツデポット
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
US7889724B2 (en) * 2007-04-13 2011-02-15 Wideorbit, Inc. Multi-station media controller
US20080255686A1 (en) * 2007-04-13 2008-10-16 Google Inc. Delivering Podcast Content
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
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
WO2008146217A2 (fr) * 2007-05-25 2008-12-04 Nokia Corporation Entité de réseau, terminal, support de stockage lisible par ordinateur et procédé pour fournir des gadgets logiciels comprenant des publicités pour des gadgets logiciels associés
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
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
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
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
US7809603B2 (en) * 2007-09-07 2010-10-05 Brand Affinity Technologies, Inc. Advertising request and rules-based content provision engine, system and method
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
US20100114719A1 (en) * 2007-09-07 2010-05-06 Ryan Steelberg Engine, system and method for generation of advertisements with endorsements and associated editorial content
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
US20090112714A1 (en) * 2007-10-31 2009-04-30 Ryan Steelberg Engine, system and method for generation of brand affinity content
US8751479B2 (en) * 2007-09-07 2014-06-10 Brand Affinity Technologies, Inc. Search and storage engine having variable indexing for information associations
US20090112700A1 (en) * 2007-10-31 2009-04-30 Ryan Steelberg System and method for brand affinity content distribution and optimization
US20100114693A1 (en) * 2007-09-07 2010-05-06 Ryan Steelberg System and method for developing software and web based applications
US20100030746A1 (en) * 2008-07-30 2010-02-04 Ryan Steelberg System and method for distributing content for use with entertainment creatives including consumer messaging
US20100114690A1 (en) * 2007-09-07 2010-05-06 Ryan Steelberg System and method for metricizing assets in a brand affinity content distribution
US20090112718A1 (en) * 2007-10-31 2009-04-30 Ryan Steelberg System and method for distributing content for use with entertainment creatives
US20100217664A1 (en) * 2007-09-07 2010-08-26 Ryan Steelberg Engine, system and method for enhancing the value of advertisements
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
US20110078003A1 (en) * 2007-09-07 2011-03-31 Ryan Steelberg System and Method for Localized Valuations of Media Assets
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
US20100131357A1 (en) * 2007-09-07 2010-05-27 Ryan Steelberg System and method for controlling user and content interactions
US20110040648A1 (en) * 2007-09-07 2011-02-17 Ryan Steelberg System and Method for Incorporating Memorabilia in a Brand Affinity Content Distribution
US20090112717A1 (en) * 2007-10-31 2009-04-30 Ryan Steelberg Apparatus, system and method for a brand affinity engine with delivery tracking and statistics
US20100274644A1 (en) * 2007-09-07 2010-10-28 Ryan Steelberg Engine, system and method for generation of brand affinity content
US20100131337A1 (en) * 2007-09-07 2010-05-27 Ryan Steelberg System and method for localized valuations of media assets
US8725563B2 (en) * 2007-09-07 2014-05-13 Brand Affinity Technologies, Inc. System and method for searching media assets
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
US20110131141A1 (en) * 2008-09-26 2011-06-02 Ryan Steelberg Advertising request and rules-based content provision engine, system and method
US20100318375A1 (en) * 2007-09-07 2010-12-16 Ryan Steelberg System and Method for Localized Valuations of Media Assets
US9294727B2 (en) 2007-10-31 2016-03-22 Veritone, Inc. System and method for creation and management of advertising inventory using metadata
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
US9824372B1 (en) 2008-02-11 2017-11-21 Google Llc Associating advertisements with videos
US8510661B2 (en) * 2008-02-11 2013-08-13 Goldspot Media End to end response enabling collection and use of customer viewing preferences statistics
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
USD627364S1 (en) 2008-04-16 2010-11-16 Stephen Martiros Animated user interface for a computer display
US20090265226A1 (en) * 2008-04-16 2009-10-22 Stephen Martiros Methods and apparatus for interactive advertising
US20090265665A1 (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
US8344233B2 (en) 2008-05-07 2013-01-01 Microsoft Corporation Scalable music recommendation by search
US8650094B2 (en) 2008-05-07 2014-02-11 Microsoft Corporation Music recommendation using emotional allocation modeling
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
WO2009151611A1 (fr) * 2008-06-12 2009-12-17 Ryan Steelberg Publicité munie d’un code à barres
US20100036906A1 (en) * 2008-08-05 2010-02-11 Google Inc. Advertisements for streaming media
JP2012503830A (ja) * 2008-09-26 2012-02-09 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 広告要求及びルールに基づくコンテンツ提供のためのエンジン、システム及び方法
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
EP2350883A4 (fr) * 2008-10-14 2012-08-22 Brand Affinity Tech Inc Appareil, système et procédé pour un moteur d'affinité avec une marque utilisant des mentions positives et négatives et une indexation
USD627366S1 (en) 2008-11-03 2010-11-16 Stephen Martiros Animated user interface for a computer display
WO2010056866A1 (fr) * 2008-11-14 2010-05-20 Brand Affinity Technologies, Inc. Système et procédé pour la distribution et l'optimisation d'un contenu par affinité de marque
US20100145784A1 (en) * 2008-12-04 2010-06-10 Doapp, Inc. Method and system for time-and location-sensitive customer loyalty rewards program
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
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
US9170995B1 (en) * 2009-03-19 2015-10-27 Google Inc. Identifying context of content items
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
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
EP2798534B1 (fr) 2011-12-31 2019-02-06 InterDigital Madison Patent Holdings Procédé et dispositif de présentation de contenu
US9952738B1 (en) * 2012-05-30 2018-04-24 Callidus Software Inc. Creation and display of dynamic content component based on a target user accessing a website
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
US10218954B2 (en) * 2013-08-15 2019-02-26 Cellular South, Inc. Video to data
US9940972B2 (en) * 2013-08-15 2018-04-10 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
WO2016044321A1 (fr) 2014-09-16 2016-03-24 Min Tang Intégration d'informations de domaine dans des transitions d'état d'un transducteur à états finis conçu pour un traitement en langage naturel
WO2016044290A1 (fr) 2014-09-16 2016-03-24 Kennewick Michael R Commerce vocal
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
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
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
US20160294763A1 (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
EP3563581A4 (fr) * 2016-12-30 2020-07-29 Social Media Broadcaster, LLC Plate-forme de distribution de contenus vidéo à mécanismes de collecte de publicité et de récompense intégrés
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
US11437029B2 (en) * 2018-06-05 2022-09-06 Voicify, LLC Voice application platform
US10636425B2 (en) 2018-06-05 2020-04-28 Voicify, LLC Voice application platform
US10803865B2 (en) 2018-06-05 2020-10-13 Voicify, LLC Voice application platform
US10235999B1 (en) 2018-06-05 2019-03-19 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
SG10202010242VA (en) * 2019-10-18 2021-05-28 Affle Int Pte Ltd Method and system for enabling an interaction between a user and a podcast

Citations (16)

* Cited by examiner, † Cited by third party
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
US20050192802A1 (en) * 2004-02-11 2005-09-01 Alex Robinson Handwriting and voice input with automatic correction
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
US7065488B2 (en) * 2000-09-29 2006-06-20 Pioneer Corporation Speech recognition system with an adaptive acoustic model

Family Cites Families (131)

* Cited by examiner, † Cited by third party
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
AU7588100A (en) 1999-09-16 2001-04-17 Ixl Enterprises, Inc. System and method for linking media content
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
WO2001035291A2 (fr) * 1999-11-10 2001-05-17 Amazon.Com, Inc. Procede et systeme servant a affecter un espace d'affichage
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
MXPA03003495A (es) 2000-10-24 2005-01-25 Thomson Licensing Sa Metodo para dimensionar una pagina de reproduccion de medios embebida.
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
US7136875B2 (en) * 2002-09-24 2006-11-14 Google, Inc. Serving advertisements based on content
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
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
AU2003263928A1 (en) 2002-08-19 2004-03-03 Choicestream Statistical personalized recommendation system
EP1395056A1 (fr) * 2002-08-30 2004-03-03 Sony International (Europe) GmbH Méthode pour créer un profil d'utilisateur et pour faire une suggestion pour une prochaine sélection de l'utilisateur
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
WO2005052738A2 (fr) 2003-11-21 2005-06-09 Marchex, Inc. Systeme et procede de publicite en ligne
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
WO2006031766A2 (fr) * 2004-09-13 2006-03-23 Manzo Anthony V Systemes et procedes pour publicites de jeux de hasard
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
EP1842369B1 (fr) * 2005-01-12 2020-04-08 Invidi Technologies Corporation Modèle d'impression ciblée pour mise à disposition d'éléments incitatifs de réseau de diffusion
CA2594929A1 (fr) 2005-01-14 2006-07-20 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
US20060212353A1 (en) * 2005-03-16 2006-09-21 Anton Roslov Targeted advertising system and method
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
IN2005MU00878A (fr) 2005-07-22 2009-06-29
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
WO2007056451A2 (fr) 2005-11-07 2007-05-18 Scanscout, Inc. Techniques de rendu d'annonces publicitaires a media enrichi
GB2435114A (en) 2006-02-08 2007-08-15 Rapid Mobile Media Ltd Providing targeted additional content
US7529741B2 (en) 2006-03-06 2009-05-05 Veveo, Inc. Methods and systems for segmenting relative user preferences into fine-grain and coarse-grain collections
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
WO2008024706A2 (fr) 2006-08-21 2008-02-28 Crazy Egg, Inc. analytique de page Web visuelle
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
US7765295B2 (en) 2006-10-25 2010-07-27 Compuware Corporation Methods and apparatus for real user monitoring
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
US9729829B2 (en) 2006-12-05 2017-08-08 Crackle, Inc. Video sharing platform providing for posting content to other websites
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
US8316392B2 (en) 2007-06-11 2012-11-20 Yahoo! Inc. Systems and methods for forecasting ad inventory
US20080319850A1 (en) 2007-06-20 2008-12-25 Sekindo Ltd Method for managing website advertising space
US20080320531A1 (en) 2007-06-25 2008-12-25 Interpols Network Incorporated Systems and methods for third-party aggregated video ratings
US20080319827A1 (en) 2007-06-25 2008-12-25 Microsoft Corporation Mining implicit behavior
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
US20090326947A1 (en) * 2008-06-27 2009-12-31 James Arnold System and method for spoken topic or criterion recognition in digital media and contextual advertising
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
US8370336B2 (en) 2009-04-08 2013-02-05 Ebay Inc. Methods and systems for deriving demand metrics used in ordering item listings presented in a search results page
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

Patent Citations (16)

* Cited by examiner, † Cited by third party
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 (40)

* Cited by examiner, † Cited by third party
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
US8577996B2 (en) 2007-09-18 2013-11-05 Tremor Video, Inc. Method and apparatus for tracing users of online video web sites
US20090083417A1 (en) * 2007-09-18 2009-03-26 John Hughes Method and apparatus for tracing users of online video web sites
US10270870B2 (en) 2007-09-18 2019-04-23 Adobe Inc. Passively monitoring online video viewing and viewer behavior
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
US9485316B2 (en) 2008-09-17 2016-11-01 Tubemogul, Inc. 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
US20110029666A1 (en) * 2008-09-17 2011-02-03 Lopatecki Jason 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
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
US9781221B2 (en) 2008-09-17 2017-10-03 Adobe Systems Incorporated 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
US9202465B2 (en) * 2011-03-25 2015-12-01 General Motors Llc Speech recognition dependent on text message content
US20120245934A1 (en) * 2011-03-25 2012-09-27 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
US10199042B2 (en) 2011-04-04 2019-02-05 Digimarc Corporation Context-based smartphone sensor logic
US10510349B2 (en) 2011-04-04 2019-12-17 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
US20210103825A1 (en) * 2016-04-08 2021-04-08 International Business Machines Corporation Generating a solution for an optimization problem
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
US10770065B2 (en) 2016-12-19 2020-09-08 Samsung Electronics Co., Ltd. Speech recognition method and apparatus
WO2018117532A1 (fr) * 2016-12-19 2018-06-28 Samsung Electronics Co., Ltd. Procédé et appareil de reconnaissance vocale
KR20180070970A (ko) * 2016-12-19 2018-06-27 삼성전자주식회사 음성 인식 방법 및 장치
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
WO2007056344A3 (fr) 2007-12-21
EP1952326A2 (fr) 2008-08-06
US20070112630A1 (en) 2007-05-17
WO2007056451A2 (fr) 2007-05-18
WO2007056344A2 (fr) 2007-05-18
WO2007056451A3 (fr) 2009-05-07
US9563826B2 (en) 2017-02-07
EP1952326A4 (fr) 2010-08-04
US20120278169A1 (en) 2012-11-01
US20170364777A1 (en) 2017-12-21
JP2009521736A (ja) 2009-06-04

Similar Documents

Publication Publication Date Title
US20070112567A1 (en) Techiques for model optimization for statistical pattern recognition
US11380330B2 (en) Conversational recovery for voice user interface
US11972753B2 (en) System and method for performing automatic speech recognition system parameter adjustment via machine learning
US10803869B2 (en) Voice enablement and disablement of speech processing functionality
US10923111B1 (en) Speech detection and speech recognition
US10410627B2 (en) Automatic language model update
CN1196105C (zh) 给用户提供声音反馈的可扩展语音识别系统
US7424427B2 (en) Systems and methods for classifying audio into broad phoneme classes
US6212498B1 (en) Enrollment in speech recognition
US9911413B1 (en) Neural latent variable model for spoken language understanding
US6442519B1 (en) Speaker model adaptation via network of similar users
US20200035231A1 (en) Anchored speech detection and speech recognition
US6424943B1 (en) Non-interactive enrollment in speech recognition
JP6550068B2 (ja) 音声認識における発音予測
CN114097026A (zh) 语音识别的上下文偏置
US10224030B1 (en) Dynamic gazetteers for personalized entity recognition
Yadava et al. A spoken query system for the agricultural commodity prices and weather information access in Kannada language
US20240029743A1 (en) Intermediate data for inter-device speech processing
CN113593523A (zh) 基于人工智能的语音检测方法、装置及电子设备
Dey et al. Cross-corpora language recognition: A preliminary investigation with Indian languages
US11935533B1 (en) Content-related actions based on context
Wester et al. A comparison of data-derived and knowledge-based modeling of pronunciation variation
US12271360B1 (en) Dynamic indexing in key-value stores
Benıtez et al. Different confidence measures for word verification in speech recognition
Chaloupka et al. Modification of the speech feature extraction module for the improvement of the system for automatic lectures transcription

Legal Events

Date Code Title Description
AS Assignment

Owner name: SCANSCOUT, INC.,MASSACHUSETTS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:LAU, WAI KIT;LEE, STEVEN;SIGNING DATES FROM 20061218 TO 20061219;REEL/FRAME:018842/0890

STCB Information on status: application discontinuation

Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION