Patents by Inventor Matthew Douglas Hoffman
Matthew Douglas Hoffman has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11562169Abstract: The present disclosure is directed towards methods and systems for determining multimodal image edits for a digital image. The systems and methods receive a digital image and analyze the digital image. The systems and methods further generate a feature vector of the digital image, wherein each value of the feature vector represents a respective feature of the digital image. Additionally, based on the feature vector and determined latent variables, the systems and methods generate a plurality of determined image edits for the digital image, which includes determining a plurality of set of potential image attribute values and selecting a plurality of sets of determined image attribute values from the plurality of sets of potential image attribute values wherein each set of determined image attribute values comprises a determined image edit of the plurality of image edits.Type: GrantFiled: February 7, 2020Date of Patent: January 24, 2023Assignee: Adobe Inc.Inventors: Stephen DiVerdi, Matthew Douglas Hoffman, Ardavan Saeedi
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Patent number: 10762135Abstract: A digital medium environment includes an asset processing application that performs editing of assets. A projection function is trained using pairs of actions pertaining to software edits, and assets resulting from the actions to learn a joint embedding between the actions and the assets. The projection function is used in the asset processing application to recommend software actions to create an asset, and also to recommend assets to demonstrate the effects of software actions. Recommendations are based on ranking distance measures that measure distances between actions representations and asset representations in a vector space.Type: GrantFiled: November 21, 2016Date of Patent: September 1, 2020Assignee: Adobe Inc.Inventors: Matthew Douglas Hoffman, Longqi Yang, Hailin Jin, Chen Fang
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Publication number: 20200175322Abstract: The present disclosure is directed towards methods and systems for determining multimodal image edits for a digital image. The systems and methods receive a digital image and analyze the digital image. The systems and methods further generate a feature vector of the digital image, wherein each value of the feature vector represents a respective feature of the digital image. Additionally, based on the feature vector and determined latent variables, the systems and methods generate a plurality of determined image edits for the digital image, which includes determining a plurality of set of potential image attribute values and selecting a plurality of sets of determined image attribute values from the plurality of sets of potential image attribute values wherein each set of determined image attribute values comprises a determined image edit of the plurality of image edits.Type: ApplicationFiled: February 7, 2020Publication date: June 4, 2020Inventors: Stephen DiVerdi, Matthew Douglas Hoffman, Ardavan Saeedi
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Patent number: 10592776Abstract: The present disclosure is directed towards methods and systems for determining multimodal image edits for a digital image. The systems and methods receive a digital image and analyze the digital image. The systems and methods further generate a feature vector of the digital image, wherein each value of the feature vector represents a respective feature of the digital image. Additionally, based on the feature vector and determined latent variables, the systems and methods generate a plurality of determined image edits for the digital image, which includes determining a plurality of set of potential image attribute values and selecting a plurality of sets of determined image attribute values from the plurality of sets of potential image attribute values wherein each set of determined image attribute values comprises a determined image edit of the plurality of image edits.Type: GrantFiled: February 8, 2017Date of Patent: March 17, 2020Assignee: ADOBE INC.Inventors: Stephen DiVerdi, Matthew Douglas Hoffman, Ardavan Saeedi
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Patent number: 10176818Abstract: Sound processing using a product-of-filters model is described. In one or more implementations, a model is formed by one or more computing devices for a time frame of sound data as a product of filters. The model is utilized by the one or more computing devices to perform one or more sound processing techniques on the time frame of the sound data.Type: GrantFiled: November 15, 2013Date of Patent: January 8, 2019Assignee: Adobe Inc.Inventors: Dawen Liang, Matthew Douglas Hoffman, Gautham J. Mysore
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Publication number: 20180225812Abstract: The present disclosure is directed towards methods and systems for determining multimodal image edits for a digital image. The systems and methods receive a digital image and analyze the digital image. The systems and methods further generate a feature vector of the digital image, wherein each value of the feature vector represents a respective feature of the digital image. Additionally, based on the feature vector and determined latent variables, the systems and methods generate a plurality of determined image edits for the digital image, which includes determining a plurality of set of potential image attribute values and selecting a plurality of sets of determined image attribute values from the plurality of sets of potential image attribute values wherein each set of determined image attribute values comprises a determined image edit of the plurality of image edits.Type: ApplicationFiled: February 8, 2017Publication date: August 9, 2018Inventors: Stephen DiVerdi, Matthew Douglas Hoffman, Ardavan Saeedi
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Publication number: 20180143988Abstract: A digital medium environment includes an asset processing application that performs editing of assets. A projection function is trained using pairs of actions pertaining to software edits, and assets resulting from the actions to learn a joint embedding between the actions and the assets. The projection function is used in the asset processing application to recommend software actions to create an asset, and also to recommend assets to demonstrate the effects of software actions. Recommendations are based on ranking distance measures that measure distances between actions representations and asset representations in a vector space.Type: ApplicationFiled: November 21, 2016Publication date: May 24, 2018Applicant: Adobe Systems IncorporatedInventors: Matthew Douglas Hoffman, Longqi Yang, Hailin Jin, Chen Fang
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Patent number: 9721202Abstract: Sound processing techniques using recurrent neural networks are described. In one or more implementations, temporal dependencies are captured in sound data that are modeled through use of a recurrent neural network (RNN). The captured temporal dependencies are employed as part of feature extraction performed using nonnegative matrix factorization (NMF). One or more sound processing techniques are performed on the sound data based at least in part on the feature extraction.Type: GrantFiled: February 21, 2014Date of Patent: August 1, 2017Assignee: Adobe Systems IncorporatedInventors: Nicolas Maurice Boulanger-Lewandowski, Gautham J. Mysore, Matthew Douglas Hoffman
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Patent number: 9607627Abstract: Sound enhancement techniques through dereverberation are described. In one or more implementations, a method is described of enhancing sound data through removal of reverberation from the sound data by one or more computing devices. The method includes obtaining a model that describes primary sound data that is to be utilized as a prior that assumes no prior knowledge about specifics of the sound data from which the reverberation is to be removed. A reverberation kernel is computed having parameters that, when applied to the model that describes the primary sound data, corresponds to the sound data from which the reverberation is to be removed. The reverberation is removed from the sound data using the reverberation kernel.Type: GrantFiled: February 5, 2015Date of Patent: March 28, 2017Assignee: Adobe Systems IncorporatedInventors: Dawen Liang, Matthew Douglas Hoffman, Gautham J. Mysore
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Patent number: 9553681Abstract: Methods and systems for source separation based on determining a number of bases for a nonnegative matrix factorization (NMF) model are disclosed. A method includes receiving, at a computing device, a mixed signal including a combination of first signal data and second signal data. The method also includes generating, by the computing device, a time-frequency representation of the mixed signal. The method further includes determining, by applying a structured stochastic variational inference (SSVI) algorithm to the NMF model, a number of bases for a dictionary of signal-related components of the mixed signal. The method uses the number of bases and the time-frequency representation to construct the dictionary and an activation matrix of weights, the weights indicating how active each one of the signal-related components is at a given time. The method then uses the dictionary and the activation matrix to separate the first signal data from the second signal data.Type: GrantFiled: February 17, 2015Date of Patent: January 24, 2017Assignee: Adobe Systems IncorporatedInventor: Matthew Douglas Hoffman
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Publication number: 20160241346Abstract: Methods and systems for source separation based on determining a number of bases for a nonnegative matrix factorization (NMF) model are disclosed. A method includes receiving, at a computing device, a mixed signal including a combination of first signal data and second signal data. The method also includes generating, by the computing device, a time-frequency representation of the mixed signal. The method further includes determining, by applying a structured stochastic variational inference (SSVI) algorithm to the NMF model, a number of bases for a dictionary of signal-related components of the mixed signal. The method uses the number of bases and the time-frequency representation to construct the dictionary and an activation matrix of weights, the weights indicating how active each one of the signal-related components is at a given time. The method then uses the dictionary and the activation matrix to separate the first signal data from the second signal data.Type: ApplicationFiled: February 17, 2015Publication date: August 18, 2016Inventor: Matthew Douglas Hoffman
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Publication number: 20160232914Abstract: Sound enhancement techniques through dereverberation are described. In one or more implementations, a method is described of enhancing sound data through removal of reverberation from the sound data by one or more computing devices. The method includes obtaining a model that describes primary sound data that is to be utilized as a prior that assumes no prior knowledge about specifics of the sound data from which the reverberation is to be removed. A reverberation kernel is computed having parameters that, when applied to the model that describes the primary sound data, corresponds to the sound data from which the reverberation is to be removed. The reverberation is removed from the sound data using the reverberation kernel.Type: ApplicationFiled: February 5, 2015Publication date: August 11, 2016Inventors: Dawen Liang, Matthew Douglas Hoffman, Gautham J. Mysore
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Publication number: 20150242180Abstract: Sound processing techniques using recurrent neural networks are described. In one or more implementations, temporal dependencies are captured in sound data that are modeled through use of a recurrent neural network (RNN). The captured temporal dependencies are employed as part of feature extraction performed using nonnegative matrix factorization (NMF). One or more sound processing techniques are performed on the sound data based at least in part on the feature extraction.Type: ApplicationFiled: February 21, 2014Publication date: August 27, 2015Inventors: Nicolas Maurice Boulanger-Lewandowski, Gautham J. Mysore, Matthew Douglas Hoffman
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Publication number: 20150142450Abstract: Sound processing using a product-of-filters model is described. In one or more implementations, a model is formed by one or more computing devices for a time frame of sound data as a product of filters. The model is utilized by the one or more computing devices to perform one or more sound processing techniques on the time frame of the sound data.Type: ApplicationFiled: November 15, 2013Publication date: May 21, 2015Applicant: Adobe Systems IncorporatedInventors: Dawen Liang, Matthew Douglas Hoffman, Gautham J. Mysore