Sound alignment may be leveraged to support a wide range of functionality. For example, sound data may be captured for use as part of a movie, recording of a song, and so on. Parts of the sound data, however, may reflect capture in a noisy environment and therefore may be less than desirable when output, such as by being difficult to understand, interfere with desired sounds, and so on. Accordingly, parts of the sound data may be replaced by other sound data using sound alignment. Sound alignment may also be employed to support other functionality, such as to utilize a foreign overdub to replace the sound data with dialogue in a different language.
However, conventional techniques that are employed to automatically align the sound data may prove inadequate when confronted with disparate types of sound data, such as to employ a foreign overdub. Accordingly, these conventional techniques may cause a user to forgo use of these techniques as the results were often inconsistent, could result in undesirable alignments that lacked realism, and so forth. This may force users to undertake multiple re-recordings of the sound data that is to be used as a replacement until a desired match is obtained, manual fixing of the timing by a sound engineer, and so on.
Time interval sound alignment techniques are described. In one or more implementations, one or more inputs are received via interaction with a user interface that indicates that a first time interval in a first representation of sound data generated from a first sound signal corresponds to a second time interval in a second representation of sound data generated from a second sound signal. A stretch value is calculated based on an amount of time represented in the first and second time intervals, respectively. Aligned sound data is generated from the sound data for the first and second time intervals based on the calculated stretch value.
This Summary introduces a selection of concepts in a simplified form that are further described below in the Detailed Description. As such, this Summary is not intended to identify essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items. Entities represented in the figures may be indicative of one or more entities and thus reference may be made interchangeably to single or plural forms of the entities in the discussion.
Sound alignment techniques may be employed to support a variety of different functionality. For example, sound data having a higher quality may be synchronized with sound data having a lower quality to replace the lower quality sound data, such as to remove noise from a video shoot, music recording, and so on. In another example, a foreign overdub may be used to replace original sound data for a movie with dialogue in a different language. However, conventional auto-alignment systems could result in an output having incorrect alignment, could consume significant amounts of computing resources, and so on, especially when confronted with sound data having significantly different spectral characteristics, such as for a foreign overdub, to remove foul language, and so on.
Time interval sound alignment techniques are described herein. In one or more implementations, a user interface is configured to enable a user to specify particular time intervals of sound data that are to be aligned to each other. A stretch value is then calculated that defines a difference in the amount of time referenced by the respective time intervals. The stretch value is then used to stretch or compress the sound data for the corresponding time intervals to generate aligned sound data. In this way, these techniques may operate to align sound data that may have different spectral characteristics as well as promote an efficient use of computing resources. Further discussion of these and other examples may be found in relation to the following sections.
In the following discussion, an example environment is first described that may employ the techniques described herein. Example procedures are then described which may be performed in the example environment as well as other environments. Consequently, performance of the example procedures is not limited to the example environment and the example environment is not limited to performance of the example procedures.
Example Environment
The computing device 102, for instance, may be configured as a desktop computer, a laptop computer, a mobile device (e.g., assuming a handheld configuration such as a tablet or mobile phone), and so forth. Thus, the computing device 102 may range from full resource devices with substantial memory and processor resources (e.g., personal computers, game consoles) to a low-resource device with limited memory and/or processing resources (e.g., mobile devices). Additionally, although a single computing device 102 is shown, the computing device 102 may be representative of a plurality of different devices, such as multiple servers utilized by a business to perform operations “over the cloud” as further described in relation to
The sound capture devices 104, 106 may also be configured in a variety of ways. Illustrated examples of one such configuration involves a standalone device but other configurations are also contemplated, such as part of a mobile phone, video camera, tablet computer, part of a desktop microphone, array microphone, and so on. Additionally, although the sound capture devices 104, 106 are illustrated separately from the computing device 102, the sound capture devices 104, 106 may be configured as part of the computing device 102, a single sound capture device may be utilized in each instance, and so on.
The sound capture devices 104, 106 are each illustrated as including respective sound capture modules 108, 110 that are representative of functionality to generate sound data, examples of which include reference sound data 112 and overdub sound data 114. Reference sound data 112 is utilized to describe sound data for which at least a part is to be replaced by the overdub sound data 114. This may include replacement of noisy portions (e.g., due to capture of the reference sound data 112 “outside”), use of a foreign overdub, and replacement using sound data that has different spectral characteristics. Thus, the overdub sound data 114 may be thought of as unaligned sound data that is to be processed for alignment with the reference sound data 112. Additionally, although illustrated separately for clarity in the discussion, it should be apparent that these roles may be satisfied alternately by different collections of sound data (e.g., in which different parts are taken from two or more files), and so on.
Regardless of where the reference sound data 112, and overdub sound data 114 originated, this data may then be obtained by the computing device 102 for processing by a sound processing module 116. Although illustrated as part of the computing device 102, functionality represented by the sound processing module 116 may be further divided, such as to be performed “over the cloud” via a network 118 connection, further discussion of which may be found in relation to
An example of functionality of the sound processing module 116 is represented as an alignment module 120. The alignment module 120 is representative of functionality to align the overdub sound data 114 to the reference sound data 112 to create aligned sound data 122. As previously described, this may be used to replace a noisy portion of sound data, replace dialogue with other dialogue (e.g., for different languages), and so forth. In order to aid in the alignment, the alignment module 120 may support an alignment user interface 124 via which user inputs may be received to indicate corresponding time intervals of the reference sound data 112 to the overdub sound data 114. Further discussion of generation of the aligned sound data 122 and interaction with the alignment user interface 124 may be found in the following discussion and associated figure.
The sound data, for instance, may be used to form one or more spectrograms of a respective signal. For example, a time-domain signal may be received and processed to produce a time-frequency representation, e.g., a spectrogram, which may be output in an alignment user interface 124 for viewing by a user. Other representations are also contemplated, such as a time domain representation, an original time domain signal, and so on. Thus, the reference sound data 112 and overdub sound data 114 may be used to provide a time-frequency representation of the reference sound signal 202 and overdub sound signal 204, respectively, in this example. Thus, the reference and overdub sound data 112, 114 may represent sound captured by the devices.
Spectrograms may be generated in a variety of ways, an example of which includes calculation as magnitudes of short time Fourier transforms (STFT) of the signals. Additionally, the spectrograms may assume a variety of configurations, such as narrowband spectrograms (e.g., 32 ms windows) although other instances are also contemplated. The STFT sub-bands may be combined in a way so as to approximate logarithmically-spaced or other nonlinearly-spaced sub-bands.
Overdub sound data 114 and reference sound data 112 are illustrated as being received for output by an alignment user interface 124. The alignment user interface 124 is configured to output representations of sound data, such as a time or time/frequency representation of the reference and overdub sound data 112, 114. In this way, a user may view characteristics of the sound data and identify different portions that may be desirable to align, such as to align sentences, phrases, and so on. A user may then interact with the alignment user interface 124 to define time intervals 208, 210 in the reference sound data 112 and the overdub sound data 114 that are to correspond to each other.
The time intervals 208, 210 may then be provided to an adjustment and synthesis module 212 to generate aligned sound data 122 from the reference and overdub sound data 114. For example, a stretch value calculation module 214 may be employed to calculate a stretch value that describes a difference between amounts of time described by the respective time intervals 208, 210. The time interval 208 of the reference sound data 112, for instance, may be 120% longer than the time interval 210 for the overdub sound data 114. Accordingly, the sound data that corresponds to the item interval 210 for the overdub sound data 114 may be stretched by this stretch value by the synthesis module 216 to form the aligned sound data 122.
Results from conventional temporal alignment techniques when applied to sound data having dissimilar spectral characteristics such as foreign overdubs could include inconsistent timing and artifacts. However, the time interval techniques described herein may be used to preserve relative timing in the overdub sound data 114, and thus avoid the inconsistent timing and artifacts of conventional frame-by-frame alignment techniques that were feature based.
For example, if the reference and overdub sound data 112, 114 include significantly different features, alignment of those features could result in inaccuracies. Such features may be computed in a variety of ways. Examples of which include use of an algorithm, such as Probabilistic Latent Component Analysis (PLCA), non-negative matrix factorization (NMF), non-negative hidden Markov (N-HMM), non-negative factorial hidden Markov (N-FHMM), and the like. The time intervals, however, may be used to indicate correspondence between phrases, sentences, and so on even if having dissimilar features and may preserve relative timing of those intervals.
Further, processing performed using the time intervals may be performed using fewer computational resources and thus may be performed with improved efficiency. For example, the longer the clip, the more likely it was to result in an incorrect alignment using conventional techniques. Second, computation time is proportionate to the length of clips, such as the length of the overdub clip times the length of the reference clip. Therefore, if the two clip lengths double, the computation time quadruples. Consequently, conventional processing could be resource intensive, which could result in delays to even achieve an undesirable result.
However, efficiency of the alignment module 120 may also be improved through use of the alignment user interface 124. Through specification of the alignment points, for instance, an alignment task for the two clips in the previous example may be divided into a plurality of interval alignment tasks. Results of the plurality of interval alignment tasks may then be combined to create aligned sound data 122 for the two clips. For example, adding “N” pairs of alignment points may increase computation speed by a factor between “N” and “N2”. An example of the alignment user interface 124 is discussed as follows and shown in a corresponding figure.
The representations 302, 304 are displayed concurrently in the alignment user interface 124 by a display device of the computing device 102, although other examples are also contemplated, such as through sequential output for display. The alignment user interface 124 is configured such that alignment points 306 may be specified to indicate correspondence of points in time between the representations 302, 304, and accordingly correspondence of sound data represented at those points in time. The alignment module 120 may then generated aligned sound data 122 as previously described based on the alignment points 306. The alignment points 306 may be specified in a variety of ways, an example of which is discussed as follows and shown in the corresponding figure.
A user, when viewing the representations 302, 304 of the reference and overdub sound signals 112, 114 may notice particular points it time that are to be aligned based on spectral characteristics as displayed in the alignment user interface 124, even if those spectral characteristics pertain to different sounds. For example, a user may note that spectral characteristics in the representations 302, 304 each pertain to the beginning of a phrase at alignment points 402, 402′. Accordingly, the user may indicate such through interaction with the alignment user interface by setting the alignment points 402, 402′. The user may repeat this by selecting additional alignment points 404, 404′, 406, 406′, 408, 408′, 410, 410′, which therefore also define a plurality of time intervals 414, 414′, 416, 416′, 418, 418′, 420, 420′, 422, 422′ as corresponding to each other.
This selection, including the order thereof, may be performed in a variety of ways. For example, a user may select an alignment point 402 in the representation 302 of the reference sound data 112 and then indicate a corresponding point in time 402′ in the representation 304 of the overdub sound signal 114. This selection may also be reversed, such as by selecting an alignment point 402′ in the representation 304 of the overdub sound data 114 and then an alignment point 402 in the representation 302 of the reference sound data 112. Thus, in both of these examples a user alternates selections between the representations 302, 304 to indicate corresponding points in time.
Other examples are also contemplated. For example, the alignment user interface 124 may also be configured to support a series of selections made through interacting with one representation (e.g., alignment point 402, 404 in representation 302) followed by a corresponding series of selections made through interacting with another representation, e.g., alignment points 402′, 404′ in representation 302. In another example, alignment points may be specified having unique display characteristics to indicate correspondence, may be performed through a drag-and-drop operations, and so on. Further, other examples are also contemplated, such as to specify the time intervals 414, 414′ themselves as corresponding to each other, for which a variety of different user interface techniques may be employed.
Regardless of a technique used to indicate the alignment points for the time intervals, a result of this manual alignment through interaction with the alignment user interface 124 indicates correspondence between the sound data. This correspondence may be leveraged to generate the aligned sound data 122. An example of the alignment user interface 124 showing a representation of the aligned sound data 122 is discussed as follows and shown in the corresponding figure.
The alignment module 120 may use this information in a variety of ways to form aligned sound data 122. For example, the alignment points may be utilized to strictly align those points in time specified by the alignment points 306 for the reference and overdub sound data 112, 114 as corresponding to each other at a beginning and end of the time intervals. The alignment module 120 may then utilize a stretch value that is computed based on the difference in the length to align sound data within the time intervals as a whole and thereby preserve relative timing within the time intervals. This may include stretching and/or compressing sound data included within the time intervals as a whole using the stretch values to arrive at aligned sound data for that interval.
Additionally, processing of the sound data by interval may be utilized to improve efficiency as previously described. The alignment module 120, for instance, may divide the alignment task for the reference sound data 112 and the overdub sound data 114 according to the specified time intervals. For example, the alignment task may be divided into “N+1” interval alignment tasks in which “N” is a number of user-defined alignment points 306. Two or more of the interval alignment tasks may also be run in parallel to further speed-up performance. Once alignment is finished for the intervals, the results may be combined to arrive at the aligned sound data 122 for the reference sound data 112 and the overdub sound data 114. In one or more implementations, a representation 502 of this result of the aligned sound data 114 may also be displayed in the alignment user interface 124.
As shown in
Example Procedures
The following discussion describes user interface techniques that may be implemented utilizing the previously described systems and devices. Aspects of each of the procedures may be implemented in hardware, firmware, or software, or a combination thereof. The procedures are shown as a set of blocks that specify operations performed by one or more devices and are not necessarily limited to the orders shown for performing the operations by the respective blocks. In portions of the following discussion, reference will be made to
A stretch value is calculated based on an amount of time represented in the first and second time intervals, respectively (block 604). For example, the time intervals may describe different amounts of time. Accordingly, the stretch value may be calculated to describe an amount of time a time interval is to be stretched or compressed as a whole to match an amount of time described by another time interval. For example, the stretch value may be used to align a time interval in the overdub sound data 114 to a time interval in the reference sound data 112.
Aligned sound data is generated from the sound data for the first and second time intervals based on the calculated stretch value (block 606). The generation may be performed without computation of features and alignment thereof as in conventional techniques, thereby preserving relative timing of the intervals. However, implementations are also contemplated in which features are also leveraged, which may be used to stretch and compress portions with the time intervals, the use of which may be constrained by a cost value to still promote preservation of the relative timing, generally.
Example System and Device
The example computing device 702 as illustrated includes a processing system 704, one or more computer-readable media 706, and one or more I/O interface 708 that are communicatively coupled, one to another. Although not shown, the computing device 702 may further include a system bus or other data and command transfer system that couples the various components, one to another. A system bus can include any one or combination of different bus structures, such as a memory bus or memory controller, a peripheral bus, a universal serial bus, and/or a processor or local bus that utilizes any of a variety of bus architectures. A variety of other examples are also contemplated, such as control and data lines.
The processing system 704 is representative of functionality to perform one or more operations using hardware. Accordingly, the processing system 704 is illustrated as including hardware element 710 that may be configured as processors, functional blocks, and so forth. This may include implementation in hardware as an application specific integrated circuit or other logic device formed using one or more semiconductors. The hardware elements 710 are not limited by the materials from which they are formed or the processing mechanisms employed therein. For example, processors may be comprised of semiconductor(s) and/or transistors (e.g., electronic integrated circuits (ICs)). In such a context, processor-executable instructions may be electronically-executable instructions.
The computer-readable storage media 706 is illustrated as including memory/storage 712. The memory/storage 712 represents memory/storage capacity associated with one or more computer-readable media. The memory/storage component 712 may include volatile media (such as random access memory (RAM)) and/or nonvolatile media (such as read only memory (ROM), Flash memory, optical disks, magnetic disks, and so forth). The memory/storage component 712 may include fixed media (e.g., RAM, ROM, a fixed hard drive, and so on) as well as removable media (e.g., Flash memory, a removable hard drive, an optical disc, and so forth). The computer-readable media 706 may be configured in a variety of other ways as further described below.
Input/output interface(s) 708 are representative of functionality to allow a user to enter commands and information to computing device 702, and also allow information to be presented to the user and/or other components or devices using various input/output devices. Examples of input devices include a keyboard, a cursor control device (e.g., a mouse), a microphone, a scanner, touch functionality (e.g., capacitive or other sensors that are configured to detect physical touch), a camera (e.g., which may employ visible or non-visible wavelengths such as infrared frequencies to recognize movement as gestures that do not involve touch), and so forth. Examples of output devices include a display device (e.g., a monitor or projector), speakers, a printer, a network card, tactile-response device, and so forth. Thus, the computing device 702 may be configured in a variety of ways as further described below to support user interaction.
Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms “module,” “functionality,” and “component” as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors.
An implementation of the described modules and techniques may be stored on or transmitted across some form of computer-readable media. The computer-readable media may include a variety of media that may be accessed by the computing device 702. By way of example, and not limitation, computer-readable media may include “computer-readable storage media” and “computer-readable signal media.”
“Computer-readable storage media” may refer to media and/or devices that enable persistent and/or non-transitory storage of information in contrast to mere signal transmission, carrier waves, or signals per se. Thus, computer-readable storage media refers to non-signal bearing media. The computer-readable storage media includes hardware such as volatile and non-volatile, removable and non-removable media and/or storage devices implemented in a method or technology suitable for storage of information such as computer readable instructions, data structures, program modules, logic elements/circuits, or other data. Examples of computer-readable storage media may include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, hard disks, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other storage device, tangible media, or article of manufacture suitable to store the desired information and which may be accessed by a computer.
“Computer-readable signal media” may refer to a signal-bearing medium that is configured to transmit instructions to the hardware of the computing device 702, such as via a network. Signal media typically may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as carrier waves, data signals, or other transport mechanism. Signal media also include any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media.
As previously described, hardware elements 710 and computer-readable media 706 are representative of modules, programmable device logic and/or fixed device logic implemented in a hardware form that may be employed in some embodiments to implement at least some aspects of the techniques described herein, such as to perform one or more instructions. Hardware may include components of an integrated circuit or on-chip system, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), a complex programmable logic device (CPLD), and other implementations in silicon or other hardware. In this context, hardware may operate as a processing device that performs program tasks defined by instructions and/or logic embodied by the hardware as well as a hardware utilized to store instructions for execution, e.g., the computer-readable storage media described previously.
Combinations of the foregoing may also be employed to implement various techniques described herein. Accordingly, software, hardware, or executable modules may be implemented as one or more instructions and/or logic embodied on some form of computer-readable storage media and/or by one or more hardware elements 710. The computing device 702 may be configured to implement particular instructions and/or functions corresponding to the software and/or hardware modules. Accordingly, implementation of a module that is executable by the computing device 702 as software may be achieved at least partially in hardware, e.g., through use of computer-readable storage media and/or hardware elements 710 of the processing system 704. The instructions and/or functions may be executable/operable by one or more articles of manufacture (for example, one or more computing devices 702 and/or processing systems 704) to implement techniques, modules, and examples described herein.
The techniques described herein may be supported by various configurations of the computing device 702 and are not limited to the specific examples of the techniques described herein. This functionality may also be implemented all or in part through use of a distributed system, such as over a “cloud” 714 via a platform 716 as described below.
The cloud 714 includes and/or is representative of a platform 716 for resources 718. The platform 716 abstracts underlying functionality of hardware (e.g., servers) and software resources of the cloud 714. The resources 718 may include applications and/or data that can be utilized while computer processing is executed on servers that are remote from the computing device 702. Resources 718 can also include services provided over the Internet and/or through a subscriber network, such as a cellular or Wi-Fi network.
The platform 716 may abstract resources and functions to connect the computing device 702 with other computing devices. The platform 716 may also serve to abstract scaling of resources to provide a corresponding level of scale to encountered demand for the resources 718 that are implemented via the platform 716. Accordingly, in an interconnected device embodiment, implementation of functionality described herein may be distributed throughout the system 700. For example, the functionality may be implemented in part on the computing device 702 as well as via the platform 716 that abstracts the functionality of the cloud 714.
Although the invention has been described in language specific to structural features and/or methodological acts, it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or acts described. Rather, the specific features and acts are disclosed as example forms of implementing the claimed invention.
Number | Name | Date | Kind |
---|---|---|---|
4550425 | Andersen et al. | Oct 1985 | A |
4591928 | Bloom et al. | May 1986 | A |
5055939 | Karamon | Oct 1991 | A |
5151998 | Capps | Sep 1992 | A |
5301109 | Landauer et al. | Apr 1994 | A |
5305420 | Nakamura et al. | Apr 1994 | A |
5325298 | Gallant | Jun 1994 | A |
5351095 | Kerdranvat | Sep 1994 | A |
5418717 | Su et al. | May 1995 | A |
5490061 | Tolin et al. | Feb 1996 | A |
5510981 | Berger et al. | Apr 1996 | A |
5642522 | Zaenen et al. | Jun 1997 | A |
5652828 | Silverman | Jul 1997 | A |
5671283 | Michener et al. | Sep 1997 | A |
5710562 | Gormish et al. | Jan 1998 | A |
5717818 | Nejime et al. | Feb 1998 | A |
5729008 | Blalock et al. | Mar 1998 | A |
5749073 | Slaney | May 1998 | A |
5802525 | Rigoutsos | Sep 1998 | A |
5842204 | Andrews et al. | Nov 1998 | A |
5950194 | Bennett et al. | Sep 1999 | A |
6122375 | Takaragi et al. | Sep 2000 | A |
6266412 | Berenzweig et al. | Jul 2001 | B1 |
6304846 | George et al. | Oct 2001 | B1 |
6316712 | Laroche | Nov 2001 | B1 |
6333983 | Enichen | Dec 2001 | B1 |
6353824 | Boguraev et al. | Mar 2002 | B1 |
6370247 | Takaragi et al. | Apr 2002 | B1 |
6442524 | Ecker et al. | Aug 2002 | B1 |
6480957 | Liao et al. | Nov 2002 | B1 |
6687671 | Gudorf et al. | Feb 2004 | B2 |
6778667 | Bakhle et al. | Aug 2004 | B1 |
6792113 | Ansell et al. | Sep 2004 | B1 |
6804355 | Graunke | Oct 2004 | B1 |
7003107 | Ananth | Feb 2006 | B2 |
7103181 | Ananth | Sep 2006 | B2 |
7142669 | Dworkin et al. | Nov 2006 | B2 |
7155440 | Kronmiller et al. | Dec 2006 | B1 |
7200226 | Bace | Apr 2007 | B2 |
7213156 | Fukuda | May 2007 | B2 |
7218733 | Li et al. | May 2007 | B2 |
7221756 | Patel et al. | May 2007 | B2 |
7269664 | Hutsch et al. | Sep 2007 | B2 |
7269854 | Simmons et al. | Sep 2007 | B2 |
7350070 | Smathers et al. | Mar 2008 | B2 |
7412060 | Fukuda | Aug 2008 | B2 |
7418100 | McGrew et al. | Aug 2008 | B2 |
7533338 | Duncan et al. | May 2009 | B2 |
7536016 | Benaloh | May 2009 | B2 |
7594176 | English | Sep 2009 | B1 |
7603563 | Ansell et al. | Oct 2009 | B2 |
7627479 | Travieso et al. | Dec 2009 | B2 |
7636691 | Maari | Dec 2009 | B2 |
7672840 | Sasaki et al. | Mar 2010 | B2 |
7680269 | Nicolai et al. | Mar 2010 | B2 |
7693278 | Hiramatsu | Apr 2010 | B2 |
7711180 | Ito et al. | May 2010 | B2 |
7715591 | Owechko et al. | May 2010 | B2 |
7757299 | Robert et al. | Jul 2010 | B2 |
7827408 | Gehringer | Nov 2010 | B1 |
7836311 | Kuriya et al. | Nov 2010 | B2 |
7861312 | Lee et al. | Dec 2010 | B2 |
7884854 | Banner et al. | Feb 2011 | B2 |
8050906 | Zimmerman et al. | Nov 2011 | B1 |
8051287 | Shetty et al. | Nov 2011 | B2 |
8082592 | Harris | Dec 2011 | B2 |
8095795 | Levy | Jan 2012 | B2 |
8099519 | Ueda | Jan 2012 | B2 |
8103505 | Silverman et al. | Jan 2012 | B1 |
8130952 | Shamoon et al. | Mar 2012 | B2 |
8184182 | Lee et al. | May 2012 | B2 |
8189769 | Ramasamy et al. | May 2012 | B2 |
8199216 | Hwang | Jun 2012 | B2 |
8205148 | Sharpe et al. | Jun 2012 | B1 |
8245033 | Shetty et al. | Aug 2012 | B1 |
8290294 | Kopf et al. | Oct 2012 | B2 |
8291219 | Eto | Oct 2012 | B2 |
8300812 | Van De Ven | Oct 2012 | B2 |
8315396 | Schreiner et al. | Nov 2012 | B2 |
8340461 | Sun et al. | Dec 2012 | B2 |
8345976 | Wang et al. | Jan 2013 | B2 |
8346751 | Jin et al. | Jan 2013 | B1 |
8417806 | Chawla et al. | Apr 2013 | B2 |
8428390 | Li et al. | Apr 2013 | B2 |
8520083 | Webster et al. | Aug 2013 | B2 |
8543386 | Oh et al. | Sep 2013 | B2 |
8571308 | Grafulla-González | Oct 2013 | B2 |
8583443 | Misawa | Nov 2013 | B2 |
8586847 | Ellis et al. | Nov 2013 | B2 |
8588551 | Joshi et al. | Nov 2013 | B2 |
8619082 | Ciurea et al. | Dec 2013 | B1 |
8675962 | Mori et al. | Mar 2014 | B2 |
8694319 | Bodin et al. | Apr 2014 | B2 |
8731913 | Zopf et al. | May 2014 | B2 |
8738633 | Sharifi et al. | May 2014 | B1 |
8751022 | Eppolito | Jun 2014 | B2 |
8805560 | Tzanetakis et al. | Aug 2014 | B1 |
8855334 | Lavine et al. | Oct 2014 | B1 |
8879731 | Schultz | Nov 2014 | B2 |
8886543 | Sharifi et al. | Nov 2014 | B1 |
8903088 | Schultz | Dec 2014 | B2 |
8914290 | Hendrickson et al. | Dec 2014 | B2 |
8953811 | Sharifi et al. | Feb 2015 | B1 |
9025822 | Jin et al. | May 2015 | B2 |
9031345 | Jin et al. | May 2015 | B2 |
9129399 | Jin et al. | Sep 2015 | B2 |
9165373 | Jin et al. | Oct 2015 | B2 |
9201580 | King | Dec 2015 | B2 |
9355649 | King et al. | May 2016 | B2 |
9451304 | King et al. | Sep 2016 | B2 |
10249321 | King et al. | Apr 2019 | B2 |
20020086269 | Shpiro | Jul 2002 | A1 |
20020097380 | Moulton | Jul 2002 | A1 |
20020099547 | Chu et al. | Jul 2002 | A1 |
20020154779 | Asano et al. | Oct 2002 | A1 |
20030028380 | Freeland et al. | Feb 2003 | A1 |
20040030656 | Kambayashi et al. | Feb 2004 | A1 |
20040122656 | Abir | Jun 2004 | A1 |
20040122662 | Crockett | Jun 2004 | A1 |
20040218834 | Bishop et al. | Nov 2004 | A1 |
20040254660 | Seefeldt | Dec 2004 | A1 |
20050015343 | Nagai et al. | Jan 2005 | A1 |
20050021323 | Li | Jan 2005 | A1 |
20050069207 | Zakrzewski et al. | Mar 2005 | A1 |
20050198448 | Fevrier | Sep 2005 | A1 |
20050201591 | Kiselewich | Sep 2005 | A1 |
20050232463 | Hirvonen et al. | Oct 2005 | A1 |
20060045211 | Oh et al. | Mar 2006 | A1 |
20060078194 | Fradkin et al. | Apr 2006 | A1 |
20060122839 | Li-Chun Wang et al. | Jun 2006 | A1 |
20060147087 | Goncalves et al. | Jul 2006 | A1 |
20060165240 | Bloom et al. | Jul 2006 | A1 |
20060173846 | Omae et al. | Aug 2006 | A1 |
20070061145 | Edgington et al. | Mar 2007 | A1 |
20070070226 | Matusik et al. | Mar 2007 | A1 |
20070087756 | Hoffberg | Apr 2007 | A1 |
20070242900 | Chen et al. | Oct 2007 | A1 |
20070273653 | Chen et al. | Nov 2007 | A1 |
20070286497 | Podilchuk | Dec 2007 | A1 |
20070291958 | Jehan | Dec 2007 | A1 |
20080120230 | Lebegue et al. | May 2008 | A1 |
20080278584 | Shih et al. | Nov 2008 | A1 |
20090055139 | Agarwal et al. | Feb 2009 | A1 |
20090110076 | Chen | Apr 2009 | A1 |
20090125726 | Iyer et al. | May 2009 | A1 |
20090259684 | Knight et al. | Oct 2009 | A1 |
20090276628 | Cho et al. | Nov 2009 | A1 |
20090279697 | Schneider | Nov 2009 | A1 |
20090290710 | Tkachenko et al. | Nov 2009 | A1 |
20090297059 | Lee et al. | Dec 2009 | A1 |
20090306972 | Opitz et al. | Dec 2009 | A1 |
20090307489 | Endoh | Dec 2009 | A1 |
20090315670 | Naressi et al. | Dec 2009 | A1 |
20100023864 | Lengeling | Jan 2010 | A1 |
20100105454 | Weber | Apr 2010 | A1 |
20100128789 | Sole et al. | May 2010 | A1 |
20100153747 | Asnaashari et al. | Jun 2010 | A1 |
20100172567 | Prokoski | Jul 2010 | A1 |
20100208779 | Park et al. | Aug 2010 | A1 |
20100246816 | Thomas et al. | Sep 2010 | A1 |
20100257368 | Yuen | Oct 2010 | A1 |
20100272311 | Nir et al. | Oct 2010 | A1 |
20100279766 | Pliska et al. | Nov 2010 | A1 |
20100322042 | Serletic et al. | Dec 2010 | A1 |
20110026596 | Hong | Feb 2011 | A1 |
20110043603 | Schechner et al. | Feb 2011 | A1 |
20110112670 | Disch et al. | May 2011 | A1 |
20110131219 | Martin-Cocher et al. | Jun 2011 | A1 |
20110161669 | Eto | Jun 2011 | A1 |
20110170784 | Tanaka et al. | Jul 2011 | A1 |
20110173208 | Vogel | Jul 2011 | A1 |
20110230987 | Anguera Miróet al. | Sep 2011 | A1 |
20110261257 | Terry et al. | Oct 2011 | A1 |
20120027295 | Shao | Feb 2012 | A1 |
20120042167 | Marking et al. | Feb 2012 | A1 |
20120046954 | Lindahl et al. | Feb 2012 | A1 |
20120105728 | Liu | May 2012 | A1 |
20120134574 | Takahashi et al. | May 2012 | A1 |
20120151320 | McClements, IV | Jun 2012 | A1 |
20120173865 | Swaminathan | Jul 2012 | A1 |
20120173880 | Swaminathan | Jul 2012 | A1 |
20120216300 | Vivolo et al. | Aug 2012 | A1 |
20120321172 | Jachalsky et al. | Dec 2012 | A1 |
20130132733 | Agrawal et al. | May 2013 | A1 |
20130136364 | Kobayashi | May 2013 | A1 |
20130142330 | Schultz | Jun 2013 | A1 |
20130142331 | Schultz | Jun 2013 | A1 |
20130173273 | Kuntz et al. | Jul 2013 | A1 |
20130191491 | Kotha et al. | Jul 2013 | A1 |
20130230247 | Schlosser et al. | Sep 2013 | A1 |
20130235201 | Kiyohara et al. | Sep 2013 | A1 |
20130290818 | Arrasvuori et al. | Oct 2013 | A1 |
20140023291 | Lin | Jan 2014 | A1 |
20140119643 | Price | May 2014 | A1 |
20140135962 | King et al. | May 2014 | A1 |
20140136976 | King et al. | May 2014 | A1 |
20140140626 | Cho | May 2014 | A1 |
20140142947 | King | May 2014 | A1 |
20140148933 | King | May 2014 | A1 |
20140152776 | Cohen | Jun 2014 | A1 |
20140153816 | Cohen | Jun 2014 | A1 |
20140168215 | Cohen | Jun 2014 | A1 |
20140169660 | Cohen | Jun 2014 | A1 |
20140177903 | Price | Jun 2014 | A1 |
20140201630 | Bryan | Jul 2014 | A1 |
20140205141 | Gao et al. | Jul 2014 | A1 |
20140254881 | Jin | Sep 2014 | A1 |
20140254882 | Jin | Sep 2014 | A1 |
20140254933 | Jin | Sep 2014 | A1 |
20140254943 | Jin | Sep 2014 | A1 |
20140310006 | Anguera Miro et al. | Oct 2014 | A1 |
Number | Date | Country |
---|---|---|
WO-2010086317 | Aug 2010 | WO |
Entry |
---|
Sonar, SONAR_X1, 2010, p. 573,595-599. |
Sonar, Sonar_X1, 2010. |
VocAlign, VocALignPro, 2005. |
VocAlign, AudioSuite Plug-In for digidesign pro tools, 2005, p. 8 and p. 23. |
Sonar, Sonar X1 reference guide, 2010, p. 573. |
“Non-Final Office Action”, U.S. Appl. No. 13/794,408, dated Sep. 10, 2014, 14 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/794,125, dated Oct. 24, 2014, 19 pages. |
Zhang, et al., “Video Dehazing with Spatial and Temporal Coherence”, The Visual Computer: International Journal of Computer Graphics—CGI'2011 Conference, vol. 27, Issue 6-8, Apr. 20, 2011, 9 pages. |
Barnes, et al., “PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing”, ACM SIGGRAPH 2009 Papers (New Orleans, Louisiana, Aug. 3-7, 2009), Aug. 3, 2009, 11 pages. |
Barnes, et al., “The Generalized PatchMatch Correspondence Algorithm”, European Conference on Computer Vision, Sep. 2010, Retrieved from <http://gfx.cs.princeton.edu/pubs/Barnes_2010_TGP/generalized_pm.pdf> on Sep. 9, 2010,Sep. 2010, 14 pages. |
Brox, et al., “Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 2011, 14 pages. |
Fattal, “Single Image Dehazing”, presented at the ACM SIGGRAPH, Los Angeles, California, 2008., 2008, 9 pages. |
He, et al., “Computing Nearest-Neighbor Fields via Propagation-Assisted KD-Trees”, CVPR 2012, 2012, 8 pages. |
He, et al., “Single Image Haze Removal Using Dark Channel Prior”, In Computer Vision and Pattern Recognition, IEEE Conference on, 2009, 2009, 8 pages. |
He, et al., “Statistics of Patch Offsets for Image Completion”, ECCV 2012, 2012, 14 pages. |
Korman, et al., “Coherency Sensitive Hashing”, ICCV 2011, 2011, 8 pages. |
Olonetsky, et al., “TreeCANN—k-d tree Coherence Approximate Nearest Neighbor algorithm”, European Conference on Computer Vision, 2012, 2012, 14 pages. |
“Sound Event Recognition With Probabilistic Distance SVMs”, IEEE TASLP 19(6), 2011, 2011. |
“Non-Final Office Action”, U.S. Appl. No. 13/310,032, dated Jan. 3, 2013, 18 pages. |
“Final Office Action”, U.S. Appl. No. 13/310,032, dated Oct. 31, 2013, 21 pages. |
“Time Domain Pitch Scaling using Synchronous Overlap and Add”, retrieved from <http://homepages.inspire.net.nz/˜jamckinnon/report/sola.htm> on Nov. 12, 2012, 3 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/309,982, dated Jan. 17, 2013, 32 pages. |
“Final Office Action”, U.S. Appl. No. 13/309,982, dated Nov. 1, 2013, 34 pages. |
“Waveform Similarity Based Overlap-Add (WSOLA)”, retrieved from <http://www.pjsip.org/pjmedia/docs/html/group_PJMED_WSOLA.htm> on Nov. 12, 2012, 4 pages. |
De et al., “Traditional (?) Implementations of a Phase-Vocoder: The Tricks of the Trade”, Proceedings of the COST G-6 Conference on Digital Audio Effects (DAFX-00), Verona, Italy, Dec. 7-9, 2000, retrieved from <http://128.112.136.35/courses/archive/spring09/cos325/Bernardini.pdf> on Nov. 12, 2012,Dec. 7, 2000, 7 pages. |
Dolson, “The Phase Vocoder: A Tutorial”, retrieved from <http://www.panix.com/˜jens/pvoc-dolson.par> on Nov. 12, 2012, 11 pages. |
Felzenszwalb, et al., “Efficient Belief Propagation for Early Vision”, International Journal of Computer Vision, 70(1), 2006, pp. 41-54. |
Gastal et al., “Shared Sampling for Real-Time Alpha Matting”, Eurographics 2010, vol. 29, No. 2, 2010, 10 pages. |
Gutierrez-Osuna, “L19: Prosodic Modificatin of Speech”, Lecture based on [Taylor, 2009, ch. 14; Holmes, 2001, ch. 5; Moulines and Charpentier, 1990], retrieved from <http://research.cs.tamu.edu/prism/lectures/sp/l19.pdf> on Nov. 12, 2012, 35 pages. |
He, et al., “Corner detector based on global and local curvature properties”, Retrieved from <http://hub.hku.hk/bitstream/10722/57246/1/142282.pdf> on Dec. 21, 2012, May 2008, 13 pages. |
He, et al., “A Global Sampling Method for Alpha Matting”, CVPR 2011, 2011, pp. 2049-2056. |
Hirsch, et al., “Fast Removal of Non-uniform Camera Shake”, Retrieved from <http://webdav.is.mpg.de/pixel/fast_removal_of_camera_shake/files/Hirsch_ICCV2011_Fast%20removal%20of%20non-uniform%20camera%20shake.pdf> on Dec. 21, 2012, 8 pages. |
Jia, “Single Image Motion Deblurring Using Transparency”, Retrieved from <http://www.cse.cuhk.edu.hk/˜leojia/all_final_papers/motion_deblur_cvpr07.pdf> on Dec. 21, 2012, 8 pages. |
Klingbeil, “SPEAR: Sinusoidal Partial Editing Analysis and Resynthesis”, retrieved from <http://www.klingbeil.com/spear/> on Nov. 12, 2012, 3 pages. |
Kubo, et al., “Characterization of the Tikhonov Regularization for Numerical Analysis of Inverse Boundary Value Problems by Using the Singular Value Decomposition”, Inverse Problems in Engineering Mechanics, 1998, 1998, pp. 337-344. |
Levin, et al., “A Closed Form Solution to Natural Image Matting”, CVPR, 2006, 2006, 8 pages. |
Levin, et al., “Image and Depth from a Conventional Camera with a Coded Aperture”, ACM Transactions on Graphics, SIGGRAPH 2007 Conference Proceedings, San Diego, CA, Retrieved from <http://groups.csail.mit.edu/graphics/CodedAperture/CodedAperture-LevinEtAl-SIGGRAPH07.pdf> on Dec. 21, 2012,2007, 9 pages. |
Li, et al., “Instructional Video Content Analysis Using Audio Information”, IEEE TASLP 14(6), 2006, 2006. |
McAulay, et al., “Speech Processing Based on a Sinusoidal Model”, The Lincoln Laboratory Journal, vol. 1, No. 2, 1998, retrieved from <http://www.II.mit.edu/publications/journal/pdf/vol01_no2/1.2.3.speechprocessing.pdf> on Nov. 12, 2012,1988, pp. 153-168. |
Moinet, et al., “PVSOLA: A Phase Vocoder with Synchronized Overlap-Add”, Proc. of the 14th Int. Conference on Digital Audio Effects (DAFx-11), Paris, France, Sep. 19-23, 2011, retrieved from <http://tcts.fpms.ac.be/publications/papers/2011/dafx2011_pvsola_amtd.pdf> on Nov. 12, 2012,Sep. 19, 2011, 7 pages. |
Park, et al., “Extracting Salient Keywords from Instructional Videos Using Joint Text, Audio and Visual Cues”, Proceedings of the Human Language Technology Conference of the North American Chapter of the ACL, Association for Computational Linguistics, 2006,Jun. 2006, pp. 109-112. |
Patton, “ELEC 484 Project—Pitch Synchronous Overlap-Add”, retrieved from <http://www.joshpatton.org/yeshua/Elec484/Elec484_files/ELEC%20484%20-%20PSOLA%20Final%20Project%20Report.pdf> on Nov. 12, 2012, 11 pages. |
Radhakrishnan, et al., “A Content-Adaptive Analysis and Representation Framework for Audio Event Discovery from “Unscripted” Multimedia”, Hindawi Publishing Corporation, EURASIP Journal on Applied Signal Processing, vol. 2006, Article ID 89013, 2006, 24 pages. |
Rodet, “Musical Sound Signal Analysis/Synthesis: Sinusoidal+Residual and Elementary Waveform Models”, TFTS'97 (IEEE Time-Frequency and Time-Scale Workshop 97), Coventry, Grande Bretagne, août, 1997, retrieved from <http://articles.ircam.fr/textes/Rodet97e/index.html> on Nov. 12, 2012,1997, 16 pages. |
Roelands, et al., “Waveform Similarity Based Overlap-Add (WSOLA) for Time-Scale Modification of Speech: Structures and Evaluation”, retrieved from <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.58.1356> on Nov. 12, 2012, 4 pages. |
Serra, “A System for Sound Analysis/Transformation/Synthesis Based on a Deterministic Plus Stochastic Decomposition”, retrieved from <https://ccrma.stanford.edu/files/papers/stanm58.pdf> on Nov. 12, 2012, Oct. 1989, 166 pages. |
Serra, “Approaches to Sinusoidal Plus Residual Modeling”, retrieved from <http://www.dtic.upf.edu/˜xserra/cursos/CCRMA-workshop/lectures/7-SMS-related-research.pdf> on Nov. 12, 2012, 21 pages. |
Serra, “Musical Sound Modeling with Sinusoids Plus Noise”, published in C. Roads, S. Pope, A. Picialli, G. De Poli, editors. 1997. “Musical Signal Processing”. Swets & Zeitlinger Publishers, retrieved from <http://web.media.mit.edu/˜tristan/Classes/MAS.945/Papers/Technical/Serra_SMS_97.pdf> on Nov. 12, 2012,1997, 25 pages. |
Smaragdis, “A Probabilistic Latent Variable Model for Acoustic Modeling”, NIPS, 2006, 6 pages. |
Smaragdis, “Supervised and Semi-Supervised Separation of Sounds from Single-Channel Mixtures”, ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation, 2007, 8 pages. |
Smith, et al., “Blue Screen Matting”, SIGGRAPH 96 Conference Proceedings, Aug. 1996, 10 pages. |
Smith “MUS421/EE367B Applications Lecture 9C: Time Scale Modification (TSM) and Frequency Scaling/Shifting”, retrieved from <https://ccrma.stanford.edu/˜jos/TSM/TSM.pdf> on Nov. 12, 2012, Mar. 8, 2012, 15 pages. |
Upperman, “Changing Pitch with PSOLA for Voice Conversion”, retrieved from <http://cnx.org/content/m12474/latest/?collection=col10379/1.1> on Nov. 12, 2012, 1 page. |
Verhelst, “Overlap-Add Methods for Time-Scaling of Speech”, retrieved from <http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.128.7991> on Nov. 12, 2012, 25 pages. |
Verhelst, et al., “An Overlap-Add Technique Based on Waveform Similarity (WSOLA) for High Quality Time-Scale Modification of Speech”, retrieved from <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.202.5460&rep=rep1&type=pdf> on Nov. 12, 2012, 4 pages. |
Yang, et al., “A Constant-Space Belief Propagation Algorithm for Stereo Matching”, CVPR, 2010, 8 pages. |
Yuan, et al., “Image Deblurring with Blurred/Noisy Image Pairs”, Proceedings of ACM SIGGRAPH, vol. 26, Issue 3, Jul. 2007, 10 pages. |
“Adobe Audion”, User Guide, 2003, 390 pages. |
“Corrected Notice of Allowance”, U.S. Appl. No. 13/794,125, dated Apr. 9, 2015, 2 pages. |
“Final Office Action”, U.S. Appl. No. 13/690,755, dated Sep. 10, 2014, 7 pages. |
“MPEG Surround Specification”, International Organization for Standardization, Coding of Moving Pictures and Audio; ISO/IEF JTC 1/SC 29/WG 11; Bangkok, Thailand, Jan. 19, 2006, 186 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/309,982, dated Mar. 24, 2014, 35 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/310,032, dated Mar. 7, 2014, 21 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/660,159, dated Oct. 1, 2014, 7 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/675,711, dated Mar. 11, 2015, 14 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/675,807, dated Dec. 17, 2014, 18 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/680,952, dated Aug. 4, 2014, 8 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/681,643, dated Jan. 7, 2015, 10 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/688,421, dated Feb. 4, 2015, 18 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/690,755, dated Mar. 28, 2014, 7 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/794,219, dated Feb. 12, 2015, 28 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/794,300, dated Mar. 11, 2015, 18 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/309,982, dated Jul. 30, 2014, 6 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/310,032, dated Aug. 26, 2014, 6 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/794,125, dated Jan. 30, 2015, 7 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/794,408, dated Feb. 4, 2015, 7 pages. |
“Restriction Requirement”, U.S. Appl. No. 13/660,159, dated Jun. 12, 2014, 6 pages. |
“Restriction Requirement”, U.S. Appl. No. 13/722,825, dated Oct. 9, 2014, 7 pages. |
“Supplemental Notice of Allowance”, U.S. Appl. No. 13/310,032, dated Nov. 3, 2014, 4 pages. |
Dong, et al., “Adaptive Object Detection and Visibility Improvement in Foggy Image”, Journal of Multimedia, vol. 6, No. 1 (2011), Feb. 14, 2011, 8 pages. |
Ioffe, “Improved Consistent Sampling, Weighted Minhash and L1 Sketching”, ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining, Dec. 2010, 10 pages. |
Jehan, “Creating Music by Listening”, In PhD Thesis of Massachusetts Institute of Technology, Retrieved from <http://web.media.mit.edu/˜tristan/Papers/PhD_Tristan.pdf>,Sep. 2005, 137 pages. |
Wu, “Fish Detection in Underwater Video of Benthic Habitats in Virgin Islands”, University of Miami, May 29, 2012, 72 pages. |
Zhu, et al., “Fusion of Time-of-Flight Depth and Stereo for High Accuracy Depth Maps”, IEEE Conference on Computer Vision and Pattern Recognition, Jun. 23, 2008, 8 pages. |
“Adobe Audition 3.0 User Guide”, 2007, 194 pages. |
“Final Office Action”, U.S. Appl. No. 13/675,711, dated Jun. 23, 2015, 14 pages. |
“Final Office Action”, U.S. Appl. No. 13/675,807, dated May 22, 2015, 24 pages. |
“Final Office Action”, U.S. Appl. No. 13/681,643, dated May 5, 2015, 14 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/794,219, dated Jun. 3, 2015, 9 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/794,300, dated May 4, 2015, 8 pages. |
“Supplemental Notice of Allowance”, U.S. Appl. No. 13/794,408, dated Apr. 17, 2015, 2 pages. |
“Corrected Notice of Allowance”, U.S. Appl. No. 13/794,300, dated Jul. 30, 2015, 2 pages. |
“Final Office Action”, U.S. Appl. No. 13/688,421, dated Jul. 29, 2015, 22 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/675,807, dated Aug. 27, 2015, 6 pages. |
“Corrected Notice of Allowance”, U.S. Appl. No. 13/794,219, dated Sep. 21, 2015, 2 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/681,643, dated Oct. 16, 2015, 27 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/675,711, dated Jan. 20, 2016, 11 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/688,421, dated Jan. 7, 2016, 20 pages. |
“Final Office Action”, U.S. Appl. No. 13/661,643, dated Mar. 15, 2016, 25 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/688,421, dated Jun. 6, 2016, 10 pages. |
“Corrected Notice of Allowance”, U.S. Appl. No. 13/688,421, dated Aug. 22, 2016, 2 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/681,643, dated Nov. 17, 2016, 23 pages. |
“Non-Final Office Action”, U.S. Appl. No. 13/681,643, dated Oct. 20, 2017, 34 pages. |
“Supplemental Notice of Allowance”, U.S. Appl. No. 13/681,643, dated Dec. 6, 2018, 10 pages. |
“Notice of Allowance”, U.S. Appl. No. 13/681,643, dated Nov. 13, 2018, 12 pages. |
“Final Office Action”, U.S. Appl. No. 13/681,643, dated Apr. 12, 2017, 40 pages. |
“Final Office Action”, U.S. Appl. No. 13/681,643, dated May 4, 2018, 24 pages. |
“Advisory Action”, U.S. Appl. No. 13/681,643, dated Jul. 24, 2018, 3 pages. |
“Supplemental Notice of Allowance”, U.S. Appl. No. 13/681,643, dated Mar. 1, 2019, 10 pages. |
Number | Date | Country | |
---|---|---|---|
20140133675 A1 | May 2014 | US |