The present invention pertains to recognition systems and particularly to biometric recognition systems. More particularly, the invention pertains to iris recognition systems.
Related applications may include U.S. patent application Ser. No. 10/979,129, filed Nov. 3, 2004, which is a continuation-in-part of U.S. patent application Ser. No. 10/655,124, filed Sep. 5, 2003; and U.S. patent application Ser. No. 11/382,373, filed May 9, 2006, which are hereby incorporated by reference.
U.S. Provisional Application No. 60/778,770, filed Mar. 3, 2006, is hereby incorporated by reference.
U.S. patent application Ser. No. 11/275,703, filed Jan. 25, 2006, is hereby incorporated by reference.
U.S. Provisional Application No. 60/647,270, filed Jan. 26, 2005, is hereby incorporated by reference.
U.S. patent application Ser. No. 11/043,366, filed Jan. 26, 2005, is hereby incorporated by reference.
U.S. patent application Ser. No. 11/372,854, filed Mar. 10, 2006, is hereby incorporated by reference.
U.S. patent application Ser. No. 11/672,108, filed Feb. 7, 2007, is hereby incorporated by reference.
U.S. patent application Ser. No. 11/675,424, filed Feb. 15, 2007 is hereby incorporated by reference.
The present invention is an iris recognition system implementing image quality metrics to assess the quality of the acquired eye image for reliable operation. Images with low image quality may be rejected or flagged based upon the application.
a, 3b and 3c show an ordinary eye image, a blurred eye image and a restored blurred eye image, respectively; and
The present system may relate to biometrics, iris recognition systems, image quality metrics, authentication, access control, monitoring, identification, and security and surveillance systems. The present system addresses specifically a preprocessing procedure that may be included prior to executing the iris recognition techniques.
An overall eye detection system is shown in
The present system may assess the quality of an eye image in real-time as a quality control procedure. This approach may allow poor image acquisition to be corrected through recapture and facilitate the acquisition of a best possible image within the capture time window configured in the system. This acquisition may result in a process for providing more good quality iris images that can improve the iris identification accuracy and the integrity of iris recognition systems.
An objective of the present invention is to define rules to assess iris image quality and use these rules as discriminators for covering poor qualities of iris images or reconfiguring the processing steps based upon the image quality assessment. With a person in the loop, it may be somewhat straightforward to ultimately assess the quality the eye image using subjective evaluation. In practice, however, subjective evaluation may lead to errors and thus tend to be impractical in view of the presently developed automated iris recognition systems. In addition, what is perceived as a good quality to the human eye does not necessary secure a reliable recognition by the present processes. Thus, the image quality may be assessed based upon specific criteria critical to a successful iris recognition processing. Like the fingerprint biometrics, iris recognition systems may have widely varying matching performance factors which depend heavily on eye image quality. The iris pattern and eye pose may have a direct effect on matcher accuracy. Therefore, operational recognition systems may require effective iris image quality metrics for image assessment even as the iris pattern is analyzed.
An automated iris recognition system may have major components which include iris localization, iris map feature extraction, encoding, and enroll/matching. In image acquisition, a digital image capturing the eye may be obtained at multiple resolutions, eye orientation and transition, under variant lighting illumination and in a noise laden environment. The feature extraction process may capture the unique texture of the iris pattern, and the encoder may encode the information into an iris barcode to expedite a matching process. The matching may involve computing a number of bits matched in the iris barcode against multiple templates of barcodes in a database. The performance of such a system may depend heavily on the various stages of the iris recognition processes, and in turn each of these processes may depend on the quality of the captured iris image. An objective image quality metric can play a variety of roles in each of the iris processing stages. Many artifacts may affect one or more of these processes.
A perfectly captured iris pattern under ideal conditions may illustrate clearly the texture of an iris that can be captured in a unique iris barcode. However, many factors such as eye closure, obscuration, an off-angle eye, occlusions, imperfect acquisition embedded in electronic noise, non-uniform illumination, different sensor wavelength sensitivity, pupil dilation, and specular light reflection may cause the captured iris map to be far from having ideal quality. Smearing, blurring, defocus (corresponding iris textures are at different depths in the acquisition scene) and poor resolution may result in the capture of very poor quality images as well as have a negative impact on iris segmentation and/or feature extraction and encoding.
Here, one may define a common framework to assess the quality of an image, develop quantitative measures that can objectively and automatically assess the quality or condition of the iris image before being processed for iris recognition, and preprocess the image for quality improvement.
Digital eye images may be subject to a wide variety of distortions during acquisitions, transmission and reproduction, any of which may result in degradation of iris recognition performance. To counter such vulnerability, the present system may have quantitative measures that can automatically assess the quality of iris images before being processed for iris recognition, and develop an appropriate set of quantitative iris image quality metrics (IIQMs). The present system may include apparatus and approaches for implementation of an appropriate set of quantitative iris image quality metrics (IIQMs). The IIQMs may be defined relative to image features based on acquisition performance. The quality of the image should correlate well with subjective iris processes. The IIQMs may be integrated into the preprocessing procedure to assess the quality of the iris image before the iris recognition process is initiated. Based upon an evaluation with these metrics, one may accept the input image, reconfigure the processing to deal with degradations, or request a new capture of the iris.
One may note various iris image quality metrics. Metrics to support automatic iris quality measurement may include eyelash/eyelid occlusion, pupil dilation, illumination, SNR, motion blur, optical defocusing, sensor noise, specular reflection, pixel count, iris texture sharpness, and so on.
There may be an interest in the modeling of image sharpness for the purpose of improving the performance of image analysis. Image quality metrics appear to be a reliable general purpose tool for iris image assessment before running an iris recognition process. To that end, a set of criteria may be defined for use with iris image quality metrics. A first criterion involves blur which may be measured using high frequency distortions from coarse to fine wavelet coefficients, or XOR-ing the resulting codes of two patches of same iris to measure discrepancy among the bits. Blur may be related to defocus. A second criterion involves defocus which may be assessed by measuring high frequency within the iris map. A third criterion involves eye closure which may be assessed using the iris inner border profile. A fourth criterion involves iris obscuration which may be assessed by computing the integral of the area between the eyelid curve and iris inner boundary. A fifth criterion involves off-angle eye (i.e., gazed eye) detection which may be assessed in the iris outer boundary shape fitting. A sixth criterion involves reflection which may be assessed using iris curve fitting and high contrast thresholding. A seventh criterion may involve excessive pupil extreme dilation which may be determined by evaluating the limits of the pupil edge detection.
Objective image quality metrics may be classified according to the availability of a non-affected image, with which the distorted image is to be compared. One may note that iris image 12 quality enhancement may include pixel processing, contrast balancing, histogram equalization, image restoration, image blind deblurring, adaptive filtering for iris texture restoration, and pose normalization.
a, 3b and 3c show an example of conditioning of an iris image 12 by rehabilator 39.
Blurring may be one of the most common forms of image distortion which can affect dramatically the performance of iris recognition. Experience may show that the effect of blurring is mostly apparent on the iris map feature extractions and encoding of the iris. The iris segmentation procedure may often be unaffected due to an existence of sufficient contrast among the iris and sclera or pupil that still permits a segmentation of the iris. The blurring phenomenon may be explained as a reduction in energy at high frequencies of the spectral domain of the image. Blurring of the iris image may occur in many different forms. The optical defocus, subject motion, and camera displacement for zooming may introduce blur distortions, which are a direct result of some technical limitation during eye acquisition.
Relative to motion blur and smearing effects, one may base a solution on high frequency distortions among the coarse to fine wavelet coefficients to detect blur by comparing the linear frequency distortion filter outputs at multiple stages of a dyadic decomposition to measure the discrepancy among the stages. An objective may be to automate these detection procedures as blurring has been proven to affect the iris matching performance. Detection of blur requires some modeling of what constitutes a blurred image and unaffected image. A hypothesis may be that image smearing leaves statistical evidence which can be exploited for detection with the aid of image quality high frequency features and multivariate regression analysis.
In another approach, instead of assessing the iris texture high frequency components, one might assess the resulting iris code directly by using two different localized patches and XOR-ing them to measure discrepancies between the corresponding bits of the two patches. Cross-matching with few discrepancies should indicate blurring effects and vice versa. Other standard quality measures may be used to measure the similarity among the two patches; the more blur the iris map is, the more similar the localized patches are. One might consider measuring the similarity of the two patches by measuring the MSE between the patches intensities, the correlation of the two patches, statistical similarity, contrast difference, or peak signal to noise ratio among the two patches. Let L(x,y), and R(x,y) present the first and second patch, one may formulate these metrics as follows.
where N(R) is the number of pixels within each patch.
Correlation measure:
where
Statistical similarity
where
High frequency and blurring metrics may be noted. It may be shown that even small amount of motion blur significantly degrades performance independent of whether images were captured from an off-angle or frontal pose.
The present system may provide an approach for quantifying the blurring effect on the iris map based on an observation that blur induces a distortion of local frequency components in the iris patterns in terms of amplitude and phase which lead to a high-frequency energy loss. The present solution may be based on high frequency distortions among the coarse and fine wavelet coefficients. This approach may be used to detect blur by comparing the linear frequency distortion filter outputs at multiple stages of a dyadic decomposition to measure the discrepancy among the scales and measure their impact on the phase. One may note that any affect on the amplitude should not have any impact if only phasor information is used to encode the iris. If no high frequency distortion measure is reported, then the iris image has already gone through blurring degradation effect. On the other hand, if a discrepancy measure is significant then this implies a distortion has occurred and the original signal has contained all its iris high frequency components with no blurring effects.
Multi-resolution analysis may provide a convenient way for representation of localized signal features such as iris texture patterns because it is widely recognized as a great way to present the localized information in the signal both in spatial and frequency domains. It is for these reasons that one may deploy wavelet decomposition as the framework for a solution presented herein. Wavelet decomposition may be better suited than Fourier transform because of the varying nature of frequency components in an iris image. In the present approach, the behavior of high frequency components at different scales in the vicinity of iris pattern features may be explored to measure the blurring amount in an image. The present approach may be based on the fact that when an image is blurred through convolution with a symmetric linear filter, the low frequency information in the Fourier domain does not necessarily change. However, the local high frequency phasor and amplitude information may be affected by the filtering mechanism. Since the present encoder may be based upon the phase information, then any blurring will directly impact the encoded iris-code.
λn=Dscr(Iw
The distortion (discrepancy) measure of the quality of iris image may be measured on the basis of the structural difference among these coarse-fine wavelet coefficients of the iris image or, in other words, the structural difference between the observed image at each scale and its filtered version.
The inner product between the unbiased variance with respect to the product of the variance may be used to quantify the structural similarity. In order to avoid instability, in case either variance is null, the quantity may be modified to
where is the σn variance at scale n, the variance σn−1 at scale (n−1), and the covariance term may be defined as
Incidentally, one may note the blur image quality assessment and that a structure comparison may be conducted on the statistical normalization of the specified iris image ROIs and thus equation (1) may imply equation (2).
The elements in the finer level may be compared prior to a decimation operation for dimensional consistency. These local statistics may be computed within a kernel L×L square window, which being convolved across the predefined regions of interest (ROIs) that represent an iris map. The width of the kernel L may be chosen to represent a typical iris texture size. At each scale, the local statistics and the distortion measured within the local window may be computed. At each level, one may require a single overall quality measure of the discrepancy. One may utilize an expected average value of measure using a mean or median to evaluate the distortion measure. In other approaches here, one may include additional classical metrics to compare the statistical difference between a coarse scale image and its filtered image at the finer scale. This is possible since the regression analysis may depict the most contributing indices to result into the final decision. In addition, it is recommended that the choice of ROIs be limited to only areas that exhibit iris textures. Multiple ROIs may be treated separately to be weighted appropriately within the regression analysis. One may identify each of iris areas to be at the inner borders of the iris.
In addition, one may combine the outcome of different scales using a multivariate regression approach on the selected quality metrics of multiple scales trained based on some predefined samples of captured irises.
One may then adopt a regression approach to combine the quality indices into a final decision measure. The present metric indices may already be scaled in equation (2) to vary between −1 and 1; thus, one may define the weighting vector based upon the LS solution being {right arrow over (ω)}=D−{right arrow over (ν)}, where D+=(DTD)−1DT, the pseudo inverse of the matrix of quality indices elements per each iris sample and per each quality index. The vector {right arrow over (ν)} may be the resulting indices for the trained set.
Testing appears to indicate that the present approach is able with reasonable accuracy to distinguish between blurred images and non-affected images.
In a different embodiment, one might decompose the two localized patches (i.e., iris regions at the left and right iris-sclera borders) using the same wavelet concept and compare the coefficients of the two decompositions at all levels. Regression combination may then be applied to the output of these structure measures similar to the above example to measure discrepancy among the two patches and not among the levels. No low pass filters are needed in this composition.
In a different example, instead of assessing the iris texture, one might assess the iris code directly using the localized patches and XOR them to measure a discrepancy among the bits, which may be aided with the following equation.
mb=Σ[M(φR)]XOR[M(φR+Δφ)]≦ηg
Motion blur and smearing effects may be related to defocusing. Defocus is isotropic in nature as pixels of an image may be smeared at all directions and be measured as a blurring effect. Defocus may be assessed by measuring high frequency contents within the iris map after using a median filter to eliminate the salt/pepper type of noise. A local phase technique may be noted. The present approach may include the XOR equation provided herein.
Eye closure and exposure of the iris map may affect primarily the segmentation modeling as it is expected to extract the pupil in its entirety to enable an analysis of the iris textures surrounding it. If the eye is not open enough or some of the pupil region is not visible, such condition may affect the localization of the iris edges or change some of the algorithms modeling assumptions.
Eye closure may be assessed using the iris inner border profile. Several parameters may be formulated to evaluate the estimated border profile including a fitness parameter to measure how far the detected curve from an elliptic like shape, and a parameter defined to measure how much of eye closure there is.
Eye closure may be assessed using the pupil profile. Several parameters may be formulated to evaluate the estimated pupil profile. It may incorporate the following formula.
In the above equation, the curve f(x,y) represents the boundary of the blob, F(x,y) is the border curve of estimated fitting shape, and Fc(x,y) is the moment center of the model shape. N in the above equation represents the length of the curve f(x,y). the operator u( ) is the step function and ε<<1 is a tolerance factor.
Another is measuring the proportion of the blob within the estimated model curve. A fitting metrics may be basically the ratio of the estimated shape surface coverage or intersection of the surface of the model and the blob over the blob surface.
where Sblob is the surface of the blob. Iris quality metrics may include iris criteria. Eye closure may be assessed using the pupil profile. The parameters may be formulated to evaluate the estimated pupil profile with the boundary elliptic profile, and the coverage of pupil parameter as noted herein.
Obscuration and occlusions, due to presence of long dense eyelashes or normal closure of eyelids, may affect dramatically the segmentation and the encoding scheme of the recognition system. Iris obscuration may be assessed by computing the integral of the area between the eyelid curve and iris inner boundary. An eyelid-eyelash obscuration assessment may assume that the eye is open enough with most of the pupil being visible but still with the eyelids or eyelashes obscuring the iris texture. The assessment of this criterion may be inherited in the present POSE segmentation technique that provides a way to detect simultaneously the edges of the iris and eyelids and or eyelashes. One may assess or measure iris obscuration by computing the integral of the area surface under the eyelash/lid detected curve and the inner iris or pupil boundary with the following equation.
Off-angle and eye gazing may be a significant concern in an iris recognition system. Eye gazing is not necessarily considered as a separate issue in the present system since off-angle eye acquisition can be like other eye acquisition here. An off-angle eye not looking forward or directly at an acquisition system may be problematic for some related iris detection mechanisms. Off-angle (gazed eyes) may be assessed in the iris outer boundary shape fitting.
Although, one may design the present iris recognition processes to handle also off-angle eyes, one may want to make an assessment of this IIQM so that special treatment is devoted to the image analysis. The present approach used to assess off-angle (gazed eyes) may be measure the shape fitness of the outer boundary of the iris to a circular shape. Here, the following equation may be noted.
A strong specular reflection may be a concern and affect the contrast of the region being shined and thus affect the segmentation approach as well as the features in the iris texture. An amount of reflection may be assessed using the iris curve fitting and the high contrast thresholding.
Pupil dilation may affect iris recognition performance; however, a good segmentation technique may handle such dilation to a certain extent. Pupil extreme dilation may be detected by evaluating the limits of the pupil edge detection. It is expected that the edges of the pupil may be detected within limits of a predefined range set for a normal range of operation of a pupil dilation. In case that the limit is reached for at all angles, this may indicate that the detected edges do not reflect the actual edges of the pupil and redefinition of the limits are necessary.
Some segmentation approaches may be designed to overcome pupil dilation. However, it has been noted that in some cases, the pupil dilation is significant enough that it may impact the segmentation. The present approach for assessing pupil dilation may be as follows. The iris map may be a region at the inner border of the iris and extend enough to cover a major segment of the iris without reaching the outer border. During inner boundary estimation, one may intentionally limit the map to a region less than the outer boundaries to avoid any obscuration or noise interference that may affect the map structure. It is expected that the edges of the pupil may be detected within limits of a predefined range defined for a normal range of operation of pupil dilation. In case that the limit is again reached for at all angles, this may indicate that the detected edges do not reflect the actual edges of the pupil and that redefinition of the limits appears necessary.
In the present specification, some of the matter may be of a hypothetical or prophetic nature although stated in another manner or tense.
Although the invention has been described with respect to at least one illustrative example, many variations and modifications will become apparent to those skilled in the art upon reading the present specification. It is therefore the intention that the appended claims be interpreted as broadly as possible in view of the prior art to include all such variations and modifications.
This application claims the benefit of U.S. Provisional Application No. 60/778,770, filed Mar. 3, 2006. This application is a continuation-in-part of U.S. patent application Ser. No. 11/275,703, filed Jan. 25, 2006, which claims the benefit of U.S. Provisional Application No. 60/647,270, filed Jan. 26, 2005. This application is a continuation-in-part of U.S. patent application Ser. No. 11/043,366, filed Jan. 26, 2005. This application is a continuation-in-part of U.S. patent application Ser. No. 11/372,854, filed Mar. 10, 2006; This application is a continuation-in-part of U.S. patent application Ser. No. 11/672,108, filed Feb. 7, 2007. This application is a continuation-in-part of U.S. patent application Ser. No. 11/675,424, filed Feb. 15, 2007.
The government may have rights in the present invention.
Number | Name | Date | Kind |
---|---|---|---|
4641349 | Flom et al. | Feb 1987 | A |
4836670 | Hutchinson | Jun 1989 | A |
5231674 | Cleveland et al. | Jul 1993 | A |
5291560 | Daugman | Mar 1994 | A |
5293427 | Ueno et al. | Mar 1994 | A |
5359382 | Uenaka | Oct 1994 | A |
5404013 | Tajima | Apr 1995 | A |
5551027 | Choy et al. | Aug 1996 | A |
5572596 | Wildes et al. | Nov 1996 | A |
5608472 | Szirth et al. | Mar 1997 | A |
5664239 | Nakata | Sep 1997 | A |
5717512 | Chmielewski, Jr. et al. | Feb 1998 | A |
5751836 | Wildes et al. | May 1998 | A |
5859686 | Aboutalib et al. | Jan 1999 | A |
5860032 | Iwane | Jan 1999 | A |
5896174 | Nakata | Apr 1999 | A |
5901238 | Matsushita | May 1999 | A |
5909269 | Isogai et al. | Jun 1999 | A |
5953440 | Zhang et al. | Sep 1999 | A |
5956122 | Doster | Sep 1999 | A |
5978494 | Zhang | Nov 1999 | A |
6005704 | Chmielewski, Jr. et al. | Dec 1999 | A |
6007202 | Apple et al. | Dec 1999 | A |
6012376 | Hanke et al. | Jan 2000 | A |
6021210 | Camus et al. | Feb 2000 | A |
6028949 | McKendall | Feb 2000 | A |
6055322 | Salganicoff et al. | Apr 2000 | A |
6064752 | Rozmus et al. | May 2000 | A |
6069967 | Rozmus et al. | May 2000 | A |
6081607 | Mori et al. | Jun 2000 | A |
6088470 | Camus et al. | Jul 2000 | A |
6091899 | Konishi et al. | Jul 2000 | A |
6101477 | Hohle et al. | Aug 2000 | A |
6104431 | Inoue et al. | Aug 2000 | A |
6108636 | Yap et al. | Aug 2000 | A |
6119096 | Mann et al. | Sep 2000 | A |
6120461 | Smyth | Sep 2000 | A |
6134339 | Luo | Oct 2000 | A |
6144754 | Okano et al. | Nov 2000 | A |
6246751 | Bergl et al. | Jun 2001 | B1 |
6247813 | Kim et al. | Jun 2001 | B1 |
6252977 | Salganicoff et al. | Jun 2001 | B1 |
6282475 | Washington | Aug 2001 | B1 |
6285505 | Melville et al. | Sep 2001 | B1 |
6285780 | Yamakita et al. | Sep 2001 | B1 |
6289113 | McHugh et al. | Sep 2001 | B1 |
6299306 | Braithwaite et al. | Oct 2001 | B1 |
6308015 | Matsumoto | Oct 2001 | B1 |
6309069 | Seal et al. | Oct 2001 | B1 |
6320610 | Van Sant et al. | Nov 2001 | B1 |
6320612 | Young | Nov 2001 | B1 |
6320973 | Suzaki et al. | Nov 2001 | B2 |
6323761 | Son | Nov 2001 | B1 |
6325765 | Hay et al. | Dec 2001 | B1 |
6330674 | Angelo et al. | Dec 2001 | B1 |
6332193 | Glass et al. | Dec 2001 | B1 |
6344683 | Kim | Feb 2002 | B1 |
6370260 | Pavlidis et al. | Apr 2002 | B1 |
6377699 | Musgrave et al. | Apr 2002 | B1 |
6393136 | Amir et al. | May 2002 | B1 |
6400835 | Lemelson et al. | Jun 2002 | B1 |
6424727 | Musgrave et al. | Jul 2002 | B1 |
6424845 | Emmoft et al. | Jul 2002 | B1 |
6433818 | Steinberg et al. | Aug 2002 | B1 |
6438752 | McClard | Aug 2002 | B1 |
6441482 | Foster | Aug 2002 | B1 |
6446045 | Stone et al. | Sep 2002 | B1 |
6483930 | Musgrave et al. | Nov 2002 | B1 |
6484936 | Nicoll et al. | Nov 2002 | B1 |
6490443 | Freeny, Jr. | Dec 2002 | B1 |
6493363 | Weaver et al. | Dec 2002 | B1 |
6493669 | Curry et al. | Dec 2002 | B1 |
6494363 | Roger et al. | Dec 2002 | B1 |
6503163 | Van Sant et al. | Jan 2003 | B1 |
6505193 | Musgrave et al. | Jan 2003 | B1 |
6506078 | Mori et al. | Jan 2003 | B1 |
6508397 | Do | Jan 2003 | B1 |
6516078 | Yang et al. | Feb 2003 | B1 |
6516087 | Camus | Feb 2003 | B1 |
6516416 | Gregg et al. | Feb 2003 | B2 |
6522772 | Morrison et al. | Feb 2003 | B1 |
6523165 | Liu et al. | Feb 2003 | B2 |
6526160 | Ito | Feb 2003 | B1 |
6532298 | Cambier et al. | Mar 2003 | B1 |
6540392 | Braithwaite | Apr 2003 | B1 |
6542624 | Oda | Apr 2003 | B1 |
6546121 | Oda | Apr 2003 | B1 |
6553494 | Glass | Apr 2003 | B1 |
6580356 | Alt et al. | Jun 2003 | B1 |
6591001 | Oda et al. | Jul 2003 | B1 |
6591064 | Higashiyama et al. | Jul 2003 | B2 |
6594377 | Kim et al. | Jul 2003 | B1 |
6594399 | Camus et al. | Jul 2003 | B1 |
6598971 | Cleveland | Jul 2003 | B2 |
6600878 | Pregara | Jul 2003 | B2 |
6614919 | Suzaki et al. | Sep 2003 | B1 |
6652099 | Chae et al. | Nov 2003 | B2 |
6674367 | Sweatte | Jan 2004 | B2 |
6690997 | Rivalto | Feb 2004 | B2 |
6708176 | Strunk et al. | Mar 2004 | B2 |
6711562 | Ross et al. | Mar 2004 | B1 |
6714665 | Hanna et al. | Mar 2004 | B1 |
6718049 | Pavlidis et al. | Apr 2004 | B2 |
6718665 | Hess et al. | Apr 2004 | B2 |
6732278 | Baird, III et al. | May 2004 | B2 |
6734783 | Anbai | May 2004 | B1 |
6745520 | Puskaric et al. | Jun 2004 | B2 |
6750435 | Ford | Jun 2004 | B2 |
6751733 | Nakamura et al. | Jun 2004 | B1 |
6753919 | Daugman | Jun 2004 | B1 |
6754640 | Bozeman | Jun 2004 | B2 |
6760467 | Min et al. | Jul 2004 | B1 |
6765470 | Shinzaki | Jul 2004 | B2 |
6766041 | Golden et al. | Jul 2004 | B2 |
6775774 | Harper | Aug 2004 | B1 |
6785406 | Kamada | Aug 2004 | B1 |
6793134 | Clark | Sep 2004 | B2 |
6819219 | Bolle et al. | Nov 2004 | B1 |
6829370 | Pavlidis et al. | Dec 2004 | B1 |
6832044 | Doi et al. | Dec 2004 | B2 |
6836554 | Bolle et al. | Dec 2004 | B1 |
6837436 | Swartz et al. | Jan 2005 | B2 |
6845879 | Park | Jan 2005 | B2 |
6853444 | Haddad | Feb 2005 | B2 |
6867683 | Calvesio et al. | Mar 2005 | B2 |
6873960 | Wood et al. | Mar 2005 | B1 |
6896187 | Stockhammer | May 2005 | B2 |
6905411 | Nguyen et al. | Jun 2005 | B2 |
6920237 | Chen et al. | Jul 2005 | B2 |
6930707 | Bates et al. | Aug 2005 | B2 |
6934849 | Kramer et al. | Aug 2005 | B2 |
6950139 | Fujinawa | Sep 2005 | B2 |
6954738 | Wang et al. | Oct 2005 | B2 |
6957341 | Rice et al. | Oct 2005 | B2 |
6972797 | Izumi | Dec 2005 | B2 |
6992562 | Fuks et al. | Jan 2006 | B2 |
7053948 | Konishi | May 2006 | B2 |
7058209 | Chen et al. | Jun 2006 | B2 |
7071971 | Elberbaum | Jul 2006 | B2 |
7084904 | Liu et al. | Aug 2006 | B2 |
7136581 | Fujii | Nov 2006 | B2 |
7183895 | Bazakos et al. | Feb 2007 | B2 |
7184577 | Chen et al. | Feb 2007 | B2 |
7197173 | Jones et al. | Mar 2007 | B2 |
7204425 | Mosher, Jr. et al. | Apr 2007 | B2 |
7239726 | Li | Jul 2007 | B2 |
7277561 | Shin | Oct 2007 | B2 |
7277891 | Howard et al. | Oct 2007 | B2 |
7298873 | Miller, Jr. et al. | Nov 2007 | B2 |
7315233 | Yuhara | Jan 2008 | B2 |
7331667 | Grotehusmann et al. | Feb 2008 | B2 |
7362210 | Bazakos et al. | Apr 2008 | B2 |
7362370 | Sakamoto et al. | Apr 2008 | B2 |
7362884 | Willis et al. | Apr 2008 | B2 |
7365771 | Kahn et al. | Apr 2008 | B2 |
7406184 | Wolff et al. | Jul 2008 | B2 |
7414648 | Imada | Aug 2008 | B2 |
7417682 | Kuwakino et al. | Aug 2008 | B2 |
7418115 | Northcott et al. | Aug 2008 | B2 |
7421097 | Hamza et al. | Sep 2008 | B2 |
7443441 | Hiraoka | Oct 2008 | B2 |
7460693 | Loy et al. | Dec 2008 | B2 |
7471451 | Dent et al. | Dec 2008 | B2 |
7486806 | Azuma et al. | Feb 2009 | B2 |
7518651 | Butterworth | Apr 2009 | B2 |
7537568 | Moehring | May 2009 | B2 |
7538326 | Johnson et al. | May 2009 | B2 |
7542945 | Thompson et al. | Jun 2009 | B2 |
7580620 | Raskar et al. | Aug 2009 | B2 |
7593550 | Hamza | Sep 2009 | B2 |
7639846 | Yoda | Dec 2009 | B2 |
7722461 | Gatto et al. | May 2010 | B2 |
7751598 | Matey et al. | Jul 2010 | B2 |
7756301 | Hamza | Jul 2010 | B2 |
7756407 | Raskar | Jul 2010 | B2 |
7761453 | Hamza | Jul 2010 | B2 |
7777802 | Shinohara et al. | Aug 2010 | B2 |
7804982 | Howard et al. | Sep 2010 | B2 |
20010026632 | Tamai | Oct 2001 | A1 |
20010027116 | Baird | Oct 2001 | A1 |
20010047479 | Bromba et al. | Nov 2001 | A1 |
20010051924 | Uberti | Dec 2001 | A1 |
20010054154 | Tam | Dec 2001 | A1 |
20020010857 | Karthik | Jan 2002 | A1 |
20020033896 | Hatano | Mar 2002 | A1 |
20020039433 | Shin | Apr 2002 | A1 |
20020040434 | Elliston et al. | Apr 2002 | A1 |
20020062280 | Zachariassen et al. | May 2002 | A1 |
20020077841 | Thompson | Jun 2002 | A1 |
20020089157 | Breed et al. | Jul 2002 | A1 |
20020106113 | Park | Aug 2002 | A1 |
20020112177 | Voltmer et al. | Aug 2002 | A1 |
20020114495 | Chen et al. | Aug 2002 | A1 |
20020130961 | Lee et al. | Sep 2002 | A1 |
20020131622 | Lee et al. | Sep 2002 | A1 |
20020139842 | Swaine | Oct 2002 | A1 |
20020140715 | Smet | Oct 2002 | A1 |
20020142844 | Kerr | Oct 2002 | A1 |
20020144128 | Rahman et al. | Oct 2002 | A1 |
20020150281 | Cho | Oct 2002 | A1 |
20020154794 | Cho | Oct 2002 | A1 |
20020158750 | Almalik | Oct 2002 | A1 |
20020164054 | McCartney et al. | Nov 2002 | A1 |
20020175182 | Matthews | Nov 2002 | A1 |
20020186131 | Fettis | Dec 2002 | A1 |
20020191075 | Doi et al. | Dec 2002 | A1 |
20020191076 | Wada et al. | Dec 2002 | A1 |
20020194128 | Maritzen et al. | Dec 2002 | A1 |
20020194131 | Dick | Dec 2002 | A1 |
20020198731 | Barnes et al. | Dec 2002 | A1 |
20030002714 | Wakiyama | Jan 2003 | A1 |
20030012413 | Kusakari et al. | Jan 2003 | A1 |
20030014372 | Wheeler et al. | Jan 2003 | A1 |
20030020828 | Ooi et al. | Jan 2003 | A1 |
20030038173 | Blackson et al. | Feb 2003 | A1 |
20030046228 | Berney | Mar 2003 | A1 |
20030053663 | Chen et al. | Mar 2003 | A1 |
20030055689 | Block et al. | Mar 2003 | A1 |
20030055787 | Fujii | Mar 2003 | A1 |
20030058492 | Wakiyama | Mar 2003 | A1 |
20030061172 | Robinson | Mar 2003 | A1 |
20030061233 | Manasse et al. | Mar 2003 | A1 |
20030065626 | Allen | Apr 2003 | A1 |
20030071743 | Seah et al. | Apr 2003 | A1 |
20030072475 | Tamori | Apr 2003 | A1 |
20030073499 | Reece | Apr 2003 | A1 |
20030074317 | Hofi | Apr 2003 | A1 |
20030074326 | Byers | Apr 2003 | A1 |
20030076161 | Tisse | Apr 2003 | A1 |
20030076300 | Lauper et al. | Apr 2003 | A1 |
20030076984 | Tisse et al. | Apr 2003 | A1 |
20030080194 | O'Hara et al. | May 2003 | A1 |
20030091215 | Lauper et al. | May 2003 | A1 |
20030092489 | Veradej | May 2003 | A1 |
20030095689 | Volkommer et al. | May 2003 | A1 |
20030098776 | Friedli | May 2003 | A1 |
20030099379 | Monk et al. | May 2003 | A1 |
20030099381 | Ohba | May 2003 | A1 |
20030103652 | Lee et al. | Jun 2003 | A1 |
20030107097 | McArthur et al. | Jun 2003 | A1 |
20030107645 | Yoon | Jun 2003 | A1 |
20030108224 | Ike | Jun 2003 | A1 |
20030108225 | Li | Jun 2003 | A1 |
20030115148 | Takhar | Jun 2003 | A1 |
20030115459 | Monk | Jun 2003 | A1 |
20030116630 | Carey et al. | Jun 2003 | A1 |
20030118212 | Min et al. | Jun 2003 | A1 |
20030118217 | Kondo et al. | Jun 2003 | A1 |
20030123711 | Kim et al. | Jul 2003 | A1 |
20030125054 | Garcia | Jul 2003 | A1 |
20030125057 | Pesola | Jul 2003 | A1 |
20030126560 | Kurapati et al. | Jul 2003 | A1 |
20030131245 | Linderman | Jul 2003 | A1 |
20030131265 | Bhakta | Jul 2003 | A1 |
20030133597 | Moore et al. | Jul 2003 | A1 |
20030140235 | Immega et al. | Jul 2003 | A1 |
20030140928 | Bui et al. | Jul 2003 | A1 |
20030141411 | Pandya et al. | Jul 2003 | A1 |
20030149881 | Patel et al. | Aug 2003 | A1 |
20030152251 | Ike | Aug 2003 | A1 |
20030152252 | Kondo et al. | Aug 2003 | A1 |
20030156741 | Lee et al. | Aug 2003 | A1 |
20030158762 | Wu | Aug 2003 | A1 |
20030158821 | Maia | Aug 2003 | A1 |
20030159051 | Hollnagel | Aug 2003 | A1 |
20030163739 | Armington et al. | Aug 2003 | A1 |
20030169334 | Braithwaite et al. | Sep 2003 | A1 |
20030169901 | Pavlidis et al. | Sep 2003 | A1 |
20030169907 | Edwards et al. | Sep 2003 | A1 |
20030173408 | Mosher, Jr. et al. | Sep 2003 | A1 |
20030174049 | Beigel et al. | Sep 2003 | A1 |
20030177051 | Driscoll et al. | Sep 2003 | A1 |
20030182151 | Taslitz | Sep 2003 | A1 |
20030182182 | Kocher | Sep 2003 | A1 |
20030189480 | Hamid | Oct 2003 | A1 |
20030189481 | Hamid | Oct 2003 | A1 |
20030191949 | Odagawa | Oct 2003 | A1 |
20030194112 | Lee | Oct 2003 | A1 |
20030195935 | Leeper | Oct 2003 | A1 |
20030198368 | Kee | Oct 2003 | A1 |
20030200180 | Phelan, III et al. | Oct 2003 | A1 |
20030210139 | Brooks et al. | Nov 2003 | A1 |
20030210802 | Schuessler | Nov 2003 | A1 |
20030218719 | Abourizk et al. | Nov 2003 | A1 |
20030225711 | Paping | Dec 2003 | A1 |
20030228898 | Rowe | Dec 2003 | A1 |
20030233556 | Angelo et al. | Dec 2003 | A1 |
20030235326 | Morikawa et al. | Dec 2003 | A1 |
20030235411 | Morikawa et al. | Dec 2003 | A1 |
20030236120 | Reece et al. | Dec 2003 | A1 |
20040001614 | Russon et al. | Jan 2004 | A1 |
20040002894 | Kocher | Jan 2004 | A1 |
20040005078 | Tillotson | Jan 2004 | A1 |
20040006553 | de Vries et al. | Jan 2004 | A1 |
20040010462 | Moon et al. | Jan 2004 | A1 |
20040012760 | Mihashi et al. | Jan 2004 | A1 |
20040019570 | Bolle et al. | Jan 2004 | A1 |
20040023664 | Mirouze et al. | Feb 2004 | A1 |
20040023709 | Beaulieu et al. | Feb 2004 | A1 |
20040025030 | Corbett-Clark et al. | Feb 2004 | A1 |
20040025031 | Ooi et al. | Feb 2004 | A1 |
20040025053 | Hayward | Feb 2004 | A1 |
20040029564 | Hodge | Feb 2004 | A1 |
20040030930 | Nomura | Feb 2004 | A1 |
20040035123 | Kim et al. | Feb 2004 | A1 |
20040037450 | Bradski | Feb 2004 | A1 |
20040039914 | Barr et al. | Feb 2004 | A1 |
20040042641 | Jakubowski | Mar 2004 | A1 |
20040044627 | Russell et al. | Mar 2004 | A1 |
20040046640 | Jourdain et al. | Mar 2004 | A1 |
20040049687 | Orsini et al. | Mar 2004 | A1 |
20040050924 | Mletzko et al. | Mar 2004 | A1 |
20040050930 | Rowe | Mar 2004 | A1 |
20040052405 | Walfridsson | Mar 2004 | A1 |
20040052418 | DeLean | Mar 2004 | A1 |
20040059590 | Mercredi et al. | Mar 2004 | A1 |
20040059953 | Purnell | Mar 2004 | A1 |
20040104266 | Bolle et al. | Jun 2004 | A1 |
20040117636 | Cheng | Jun 2004 | A1 |
20040133804 | Smith et al. | Jul 2004 | A1 |
20040146187 | Jeng | Jul 2004 | A1 |
20040148526 | Sands et al. | Jul 2004 | A1 |
20040160518 | Park | Aug 2004 | A1 |
20040162870 | Matsuzaki et al. | Aug 2004 | A1 |
20040162984 | Freeman et al. | Aug 2004 | A1 |
20040169817 | Grotehusmann et al. | Sep 2004 | A1 |
20040172541 | Ando et al. | Sep 2004 | A1 |
20040174070 | Voda et al. | Sep 2004 | A1 |
20040190759 | Caldwell | Sep 2004 | A1 |
20040193893 | Braithwaite et al. | Sep 2004 | A1 |
20040204711 | Jackson | Oct 2004 | A1 |
20040219902 | Lee et al. | Nov 2004 | A1 |
20040233038 | Beenau et al. | Nov 2004 | A1 |
20040252866 | Tisse et al. | Dec 2004 | A1 |
20040255168 | Murashita et al. | Dec 2004 | A1 |
20050008200 | Azuma et al. | Jan 2005 | A1 |
20050008201 | Lee et al. | Jan 2005 | A1 |
20050012817 | Hampapur et al. | Jan 2005 | A1 |
20050029353 | Isemura et al. | Feb 2005 | A1 |
20050052566 | Kato | Mar 2005 | A1 |
20050055582 | Bazakos et al. | Mar 2005 | A1 |
20050063567 | Saitoh et al. | Mar 2005 | A1 |
20050084137 | Kim et al. | Apr 2005 | A1 |
20050084179 | Hanna et al. | Apr 2005 | A1 |
20050099288 | Spitz et al. | May 2005 | A1 |
20050102502 | Sagen | May 2005 | A1 |
20050110610 | Bazakos et al. | May 2005 | A1 |
20050125258 | Yellin et al. | Jun 2005 | A1 |
20050127161 | Smith et al. | Jun 2005 | A1 |
20050129286 | Hekimian | Jun 2005 | A1 |
20050134796 | Zelvin et al. | Jun 2005 | A1 |
20050138385 | Friedli et al. | Jun 2005 | A1 |
20050138387 | Lam et al. | Jun 2005 | A1 |
20050146640 | Shibata | Jul 2005 | A1 |
20050151620 | Neumann | Jul 2005 | A1 |
20050152583 | Kondo et al. | Jul 2005 | A1 |
20050193212 | Yuhara | Sep 2005 | A1 |
20050199708 | Friedman | Sep 2005 | A1 |
20050206501 | Farhat | Sep 2005 | A1 |
20050206502 | Bernitz | Sep 2005 | A1 |
20050207614 | Schonberg et al. | Sep 2005 | A1 |
20050210267 | Sugano et al. | Sep 2005 | A1 |
20050210270 | Rohatgi et al. | Sep 2005 | A1 |
20050210271 | Chou et al. | Sep 2005 | A1 |
20050238214 | Matsuda et al. | Oct 2005 | A1 |
20050240778 | Saito | Oct 2005 | A1 |
20050248725 | Ikoma et al. | Nov 2005 | A1 |
20050249385 | Kondo et al. | Nov 2005 | A1 |
20050255840 | Markham | Nov 2005 | A1 |
20060093190 | Cheng et al. | May 2006 | A1 |
20060147094 | Yoo | Jul 2006 | A1 |
20060165266 | Hamza | Jul 2006 | A1 |
20060274919 | LoIacono et al. | Dec 2006 | A1 |
20070036397 | Hamza | Feb 2007 | A1 |
20070140531 | Hamza | Jun 2007 | A1 |
20070160266 | Jones et al. | Jul 2007 | A1 |
20070189582 | Hamza et al. | Aug 2007 | A1 |
20070206840 | Jacobson | Sep 2007 | A1 |
20070211924 | Hamza | Sep 2007 | A1 |
20070274571 | Hamza | Nov 2007 | A1 |
20070286590 | Terashima | Dec 2007 | A1 |
20080005578 | Shafir | Jan 2008 | A1 |
20080075334 | Determan et al. | Mar 2008 | A1 |
20080075441 | Jelinek et al. | Mar 2008 | A1 |
20080104415 | Palti-Wasserman et al. | May 2008 | A1 |
20080148030 | Goffin | Jun 2008 | A1 |
20080211347 | Wright et al. | Sep 2008 | A1 |
20080252412 | Larsson et al. | Oct 2008 | A1 |
20080267456 | Anderson | Oct 2008 | A1 |
20090046899 | Northcott et al. | Feb 2009 | A1 |
20090092283 | Whillock et al. | Apr 2009 | A1 |
20090316993 | Brasnett et al. | Dec 2009 | A1 |
20100002913 | Hamza | Jan 2010 | A1 |
20100033677 | Jelinek | Feb 2010 | A1 |
20100034529 | Jelinek | Feb 2010 | A1 |
20100142765 | Hamza | Jun 2010 | A1 |
20100182440 | McCloskey | Jul 2010 | A1 |
20100239119 | Bazakos et al. | Sep 2010 | A1 |
Number | Date | Country |
---|---|---|
0484076 | May 1992 | EP |
0593386 | Apr 1994 | EP |
0878780 | Nov 1998 | EP |
0899680 | Mar 1999 | EP |
0910986 | Apr 1999 | EP |
0962894 | Dec 1999 | EP |
1018297 | Jul 2000 | EP |
1024463 | Aug 2000 | EP |
1028398 | Aug 2000 | EP |
1041506 | Oct 2000 | EP |
1041523 | Oct 2000 | EP |
1126403 | Aug 2001 | EP |
1139270 | Oct 2001 | EP |
1237117 | Sep 2002 | EP |
1477925 | Nov 2004 | EP |
1635307 | Mar 2006 | EP |
2369205 | May 2002 | GB |
2371396 | Jul 2002 | GB |
2375913 | Nov 2002 | GB |
2402840 | Dec 2004 | GB |
2411980 | Sep 2005 | GB |
9161135 | Jun 1997 | JP |
9198545 | Jul 1997 | JP |
9201348 | Aug 1997 | JP |
9147233 | Sep 1997 | JP |
9234264 | Sep 1997 | JP |
9305765 | Nov 1997 | JP |
9319927 | Dec 1997 | JP |
10021392 | Jan 1998 | JP |
10040386 | Feb 1998 | JP |
10049728 | Feb 1998 | JP |
10137219 | May 1998 | JP |
10137221 | May 1998 | JP |
10137222 | May 1998 | JP |
10137223 | May 1998 | JP |
10248827 | Sep 1998 | JP |
10269183 | Oct 1998 | JP |
11047117 | Feb 1999 | JP |
11089820 | Apr 1999 | JP |
11200684 | Jul 1999 | JP |
11203478 | Jul 1999 | JP |
11213047 | Aug 1999 | JP |
11339037 | Dec 1999 | JP |
2000005149 | Jan 2000 | JP |
2000005150 | Jan 2000 | JP |
2000011163 | Jan 2000 | JP |
2000023946 | Jan 2000 | JP |
2000083930 | Mar 2000 | JP |
2000102510 | Apr 2000 | JP |
2000102524 | Apr 2000 | JP |
2000105830 | Apr 2000 | JP |
2000107156 | Apr 2000 | JP |
2000139878 | May 2000 | JP |
2000155863 | Jun 2000 | JP |
2000182050 | Jun 2000 | JP |
2000185031 | Jul 2000 | JP |
2000194972 | Jul 2000 | JP |
2000237167 | Sep 2000 | JP |
2000242788 | Sep 2000 | JP |
2000259817 | Sep 2000 | JP |
2000356059 | Dec 2000 | JP |
2000357232 | Dec 2000 | JP |
2001005948 | Jan 2001 | JP |
2001067399 | Mar 2001 | JP |
2001101429 | Apr 2001 | JP |
2001167275 | Jun 2001 | JP |
2001222661 | Aug 2001 | JP |
2001292981 | Oct 2001 | JP |
2001297177 | Oct 2001 | JP |
2001358987 | Dec 2001 | JP |
2002119477 | Apr 2002 | JP |
2002133415 | May 2002 | JP |
2002153444 | May 2002 | JP |
2002153445 | May 2002 | JP |
2002260071 | Sep 2002 | JP |
2002271689 | Sep 2002 | JP |
2002286650 | Oct 2002 | JP |
2002312772 | Oct 2002 | JP |
2002329204 | Nov 2002 | JP |
2003006628 | Jan 2003 | JP |
2003036434 | Feb 2003 | JP |
2003108720 | Apr 2003 | JP |
2003108983 | Apr 2003 | JP |
2003132355 | May 2003 | JP |
2003150942 | May 2003 | JP |
2003153880 | May 2003 | JP |
2003242125 | Aug 2003 | JP |
2003271565 | Sep 2003 | JP |
2003271940 | Sep 2003 | JP |
2003308522 | Oct 2003 | JP |
2003308523 | Oct 2003 | JP |
2003317102 | Nov 2003 | JP |
2003331265 | Nov 2003 | JP |
2004005167 | Jan 2004 | JP |
2004021406 | Jan 2004 | JP |
2004030334 | Jan 2004 | JP |
2004038305 | Feb 2004 | JP |
2004094575 | Mar 2004 | JP |
2004152046 | May 2004 | JP |
2004163356 | Jun 2004 | JP |
2004164483 | Jun 2004 | JP |
2004171350 | Jun 2004 | JP |
2004171602 | Jun 2004 | JP |
2004206444 | Jul 2004 | JP |
2004220376 | Aug 2004 | JP |
2004261515 | Sep 2004 | JP |
2004280221 | Oct 2004 | JP |
2004280547 | Oct 2004 | JP |
2004287621 | Oct 2004 | JP |
2004315127 | Nov 2004 | JP |
2004318248 | Nov 2004 | JP |
2005004524 | Jan 2005 | JP |
2005011207 | Jan 2005 | JP |
2005025577 | Jan 2005 | JP |
2005038257 | Feb 2005 | JP |
2005062990 | Mar 2005 | JP |
2005115961 | Apr 2005 | JP |
2005148883 | Jun 2005 | JP |
2005242677 | Sep 2005 | JP |
WO 9717674 | May 1997 | WO |
WO 9721188 | Jun 1997 | WO |
WO 9802083 | Jan 1998 | WO |
WO 9808439 | Mar 1998 | WO |
WO 9932317 | Jul 1999 | WO |
WO 9952422 | Oct 1999 | WO |
WO 9965175 | Dec 1999 | WO |
WO 0028484 | May 2000 | WO |
WO 0029986 | May 2000 | WO |
WO 0031677 | Jun 2000 | WO |
WO 0036605 | Jun 2000 | WO |
WO 0062239 | Oct 2000 | WO |
WO 0101329 | Jan 2001 | WO |
WO 0103100 | Jan 2001 | WO |
WO 0128476 | Apr 2001 | WO |
WO 0135348 | May 2001 | WO |
WO 0135349 | May 2001 | WO |
WO 0140982 | Jun 2001 | WO |
WO 0163994 | Aug 2001 | WO |
WO 0169490 | Sep 2001 | WO |
WO 0186599 | Nov 2001 | WO |
WO 0201451 | Jan 2002 | WO |
WO 0219030 | Mar 2002 | WO |
WO 0235452 | May 2002 | WO |
WO 0235480 | May 2002 | WO |
WO 02091735 | Nov 2002 | WO |
WO 02095657 | Nov 2002 | WO |
WO 03002387 | Jan 2003 | WO |
WO 03003910 | Jan 2003 | WO |
WO 03054777 | Jul 2003 | WO |
WO 03077077 | Sep 2003 | WO |
WO 2004029863 | Apr 2004 | WO |
WO 2004042646 | May 2004 | WO |
WO 2004055737 | Jul 2004 | WO |
WO 2004089214 | Oct 2004 | WO |
WO 2004097743 | Nov 2004 | WO |
WO 2005008567 | Jan 2005 | WO |
WO 2005013181 | Feb 2005 | WO |
WO 2005024698 | Mar 2005 | WO |
WO 2005024708 | Mar 2005 | WO |
WO 2005024709 | Mar 2005 | WO |
WO 2005029388 | Mar 2005 | WO |
WO 2005062235 | Jul 2005 | WO |
WO 2005069252 | Jul 2005 | WO |
WO 2005093510 | Oct 2005 | WO |
WO 2005093681 | Oct 2005 | WO |
WO 2005096962 | Oct 2005 | WO |
WO 2005098531 | Oct 2005 | WO |
WO 2005104704 | Nov 2005 | WO |
WO 2005109344 | Nov 2005 | WO |
WO 2006012645 | Feb 2006 | WO |
WO 2006023046 | Mar 2006 | WO |
WO 2006051462 | May 2006 | WO |
WO 2006063076 | Jun 2006 | WO |
2006081505 | Aug 2006 | WO |
WO 2006081209 | Aug 2006 | WO |
WO 2007101269 | Sep 2007 | WO |
WO 2007101275 | Sep 2007 | WO |
WO 2007101276 | Sep 2007 | WO |
WO 2007103698 | Sep 2007 | WO |
WO 2007103701 | Sep 2007 | WO |
WO 2007103833 | Sep 2007 | WO |
WO 2007103834 | Sep 2007 | WO |
WO 2008016724 | Feb 2008 | WO |
WO 2008019168 | Feb 2008 | WO |
WO 2008019169 | Feb 2008 | WO |
WO 2008021584 | Feb 2008 | WO |
WO 2008031089 | Mar 2008 | WO |
WO 2008040026 | Apr 2008 | WO |
Number | Date | Country | |
---|---|---|---|
20070274570 A1 | Nov 2007 | US |
Number | Date | Country | |
---|---|---|---|
60778770 | Mar 2006 | US | |
60647270 | Jan 2005 | US |
Number | Date | Country | |
---|---|---|---|
Parent | 11275703 | Jan 2006 | US |
Child | 11681614 | US | |
Parent | 11681614 | US | |
Child | 11681614 | US | |
Parent | 11043366 | Jan 2005 | US |
Child | 11681614 | US | |
Parent | 11372854 | Mar 2006 | US |
Child | 11043366 | US | |
Parent | 11672108 | Feb 2007 | US |
Child | 11372854 | US | |
Parent | 11675424 | Feb 2007 | US |
Child | 11672108 | US |