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公开(公告)号：US10832380B2
公开(公告)日：2020-11-10
申请号：US15997564
申请日：2018-06-04
Applicant: Disney Enterprises, Inc.
Inventor： Steven Chapman , Mehul Patel , Joseph Popp , Ty Popko , Erika Doggett
Abstract: Systems and methods for correcting color of uncalibrated material is disclosed. Example embodiments include a system to correct color of uncalibrated material. The system may include a non-transitory computer-readable medium operatively coupled to processors. The non-transitory computer-readable medium may store instructions that, when executed cause the processors to perform a number of operations. One operation is to obtain a target image of a degraded target material with one or more objects. The degraded target material comprises degraded colors and light information corresponding to light sources in the degraded target material. Another operations is to obtain color reference data. Another operation is to identify an object in the target image that corresponds to the color reference data. Yet another operation is to correct the identified object in the target image. Another operation is to correct the target image.
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公开(公告)号：US10691894B2
公开(公告)日：2020-06-23
申请号：US15968685
申请日：2018-05-01
Applicant: Disney Enterprises, Inc.
Inventor： Erika Doggett
IPC: G06F17/27 , G06N3/08 , G06F16/332 , G06F16/33 , G06F17/28 , G10L15/22 , G06F40/30 , G06F40/56 , G06F40/268
Abstract: A process receives a user input in a human-to-machine interaction. The process generates, with a natural language generation engine, one or more response candidates. Further, the process measures, with the natural language generation engine, the semantic similarity of the one or more response candidates. In addition, the process selects, with the natural language generation engine, a response candidate from the one or more response candidates. The process measures, with the natural language generation engine, an offensiveness measurement and a politeness measurement of the selected response. The process determines, with the natural language generation engine, that the offensiveness measurement or the politeness measurement lacks compliance with one or more predefined criteria. The process selects, with the natural language generation engine, an additional response candidate from the one or more response candidates that has a higher semantic similarity measurement than remaining response candidates from the one or more response candidates.
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公开(公告)号：US11983906B2
公开(公告)日：2024-05-14
申请号：US17704907
申请日：2022-03-25
Applicant: DISNEY ENTERPRISES, INC.
Inventor： Christopher Schroers , Erika Doggett , Stephan Mandt , Jared Mcphillen , Scott Labrozzi , Romann Weber , Mauro Bamert
Abstract: Systems and methods for predicting a target set of pixels are disclosed. In one embodiment, a method may include obtaining target content. The target content may include a target set of pixels to be predicted. The method may also include convolving the target set of pixels to generate an estimated set of pixels. The method may include matching a second set of pixels in the target content to the target set of pixels. The second set of pixels may be within a distance from the target set of pixels. The method may include refining the estimated set of pixels to generate a refined set of pixels using a second set of pixels in the target content.
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公开(公告)号：US10832383B2
公开(公告)日：2020-11-10
申请号：US16167388
申请日：2018-10-22
Applicant: Disney Enterprises, Inc.
Inventor： Christopher Schroers , Mauro Bamert , Erika Doggett , Jared McPhillen , Scott Labrozzi , Romann Weber
Abstract: Systems and methods for distortion removal at multiple quality levels are disclosed. In one embodiment, a method may include receiving training content. The training content may include original content, reconstructed content, and training distortion quality levels corresponding to the reconstructed content. The reconstructed content may be derived from distorted original content. The method may also include training distortion quality levels corresponding to the reconstructed content. The method may further include receiving an initial distortion removal model. The method may include generating a conditioned distortion removal model by training the initial distortion removal model using the training content. The method may further include storing the conditioned distortion removal model.
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公开(公告)号：US20190340238A1
公开(公告)日：2019-11-07
申请号：US15968685
申请日：2018-05-01
Applicant: Disney Enterprises, Inc.
Inventor： Erika Doggett
Abstract: A process receives a user input in a human-to-machine interaction. The process generates, with a natural language generation engine, one or more response candidates. Further, the process measures, with the natural language generation engine, the semantic similarity of the one or more response candidates. In addition, the process selects, with the natural language generation engine, a response candidate from the one or more response candidates. The process measures, with the natural language generation engine, an offensiveness measurement and a politeness measurement of the selected response. The process determines, with the natural language generation engine, that the offensiveness measurement or the politeness measurement lacks compliance with one or more predefined criteria. The process selects, with the natural language generation engine, an additional response candidate from the one or more response candidates that has a higher semantic similarity measurement than remaining response candidates from the one or more response candidates.
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Patent Agency Ranking
公开(公告)号：US11335034B2
公开(公告)日：2022-05-17
申请号：US16249861
申请日：2019-01-16
Applicant: Disney Enterprises, Inc.
Inventor： Christopher Schroers , Erika Doggett , Stephan Marcel Mandt , Jared McPhillen , Scott Labrozzi , Romann Weber , Mauro Bamert
Abstract: Systems and methods for predicting a target set of pixels are disclosed. In one embodiment, a method may include obtaining target content. The target content may include a target set of pixels to be predicted. The method may also include convolving the target set of pixels to generate an estimated set of pixels. The method may include matching a second set of pixels in the target content to the target set of pixels. The second set of pixels may be within a distance from the target set of pixels. The method may include refining the estimated set of pixels to generate a refined set of pixels using a second set of pixels in the target content.
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公开(公告)号：US11151186B2
公开(公告)日：2021-10-19
申请号：US16011514
申请日：2018-06-18
Applicant: Disney Enterprises, Inc.
Inventor： Erika Doggett , Alethia Shih
IPC: G06F16/435 , G06F16/48 , G06F16/438 , G06F40/205
Abstract: Systems, devices, and methods are disclosed for presenting an interactive narrative. An apparatus includes a user interface. The apparatus also includes one or more processors operatively coupled to the user interface and a non-transitory computer-readable medium. The non-transitory computer-readable medium stores instructions that, when executed, cause the one or more processors to present a first piece of content corresponding to a given narrative via the user interface. The given narrative includes one or more characteristics. The one or more processors are caused to receive user input via the user interface. The one or more processors are caused to classify the user input into one of a plurality of response models. The one or more processors are caused to dynamically respond to the user input by presenting a second piece of content. The second piece of content is based on a selected response model corresponding to the user input.
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公开(公告)号：US11080835B2
公开(公告)日：2021-08-03
申请号：US16243650
申请日：2019-01-09
Applicant: Disney Enterprises, Inc.
Inventor： Erika Doggett , Anna Wolak , Penelope Daphne Tsatsoulis , Nicholas McCarthy , Stephan Mandt
Abstract: A process receives, with a processor, video content. Further, the process splices, with the processor, the video content into a plurality of video frames. In addition, the process splices, with the processor, at least one of the plurality of video frames into a plurality of image patches. Moreover, the process performs, with a neural network, an image reconstruction of at least one of the plurality of image patches to generate a reconstructed image patch. The process also compares, with the processor, the reconstructed image patch with the at least one of the plurality of image patches. Finally, the process determines, with the processor, a pixel error within the at least one of the plurality of image patches based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches.
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公开(公告)号：US20200219245A1
公开(公告)日：2020-07-09
申请号：US16243650
申请日：2019-01-09
Applicant: Disney Enterprises, Inc.
Inventor： Erika Doggett , Anna Wolak , Penelope Daphne Tsatsoulis , Nicholas McCarthy , Stephan Mandt
Abstract: A process receives, with a processor, video content. Further, the process splices, with the processor, the video content into a plurality of video frames. In addition, the process splices, with the processor, at least one of the plurality of video frames into a plurality of image patches. Moreover, the process performs, with a neural network, an image reconstruction of at least one of the plurality of image patches to generate a reconstructed image patch. The process also compares, with the processor, the reconstructed image patch with the at least one of the plurality of image patches. Finally, the process determines, with the processor, a pixel error within the at least one of the plurality of image patches based on a discrepancy between the reconstructed image patch and the at least one of the plurality of image patches.
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公开(公告)号：US20190333190A1
公开(公告)日：2019-10-31
申请号：US16167388
申请日：2018-10-22
Applicant: Disney Enterprises, Inc.
Inventor： Christopher Schroers , Mauro Bamert , Erika Doggett , Jared McPhillen , Scott Labrozzi , Romann Weber
Abstract: Systems and methods for distortion removal at multiple quality levels are disclosed. In one embodiment, a method may include receiving training content. The training content may include original content, reconstructed content, and training distortion quality levels corresponding to the reconstructed content. The reconstructed content may be derived from distorted original content. The method may also include training distortion quality levels corresponding to the reconstructed content. The method may further include receiving an initial distortion removal model. The method may include generating a conditioned distortion removal model by training the initial distortion removal model using the training content. The method may further include storing the conditioned distortion removal model.
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