Method of determining an obsolescence rate of a technology

Information

  • Patent Grant
  • 7949581
  • Patent Number
    7,949,581
  • Date Filed
    Thursday, September 7, 2006
    18 years ago
  • Date Issued
    Tuesday, May 24, 2011
    13 years ago
Abstract
Methods for constructing an estimated depreciated schedule for a patent are disclosed. The steps for constructing this schedule may include: (1) determining a first function which approximately describes the rate of initial increase in expected forward patent citations over time; (2) determining a second function which approximately describes the rate of eventual decay in expected forward patent citations over time; and (3) constructing an estimated depreciation schedule using a calculated decay coefficient derived from said second function.
Description
BACKGROUND OF THE INVENTION

1. Field of the Invention


The present invention in various embodiments relates to assessing the value of assets.


2. Description of the Related Art


Patents play an important role in our economy in encouraging private investment in new ideas and the development of new technologies that improve productivity and quality of life for everyone. Each year more than a quarter-million patent applications are filed in the United States Patent and Trademark Office (“PTO”), resulting in the issuance of over a hundred fifty-thousand patents annually. Patent owners and applicants pay combined fees and costs of over a billion dollars per year to the PTO to obtain and maintain their patents and applications. See, United States Patent & Trademark Office, FY 2000 USPTO Annual Report. Additional fees and costs are typically incurred for related professional services, such as attorneys fees, search fees, drafting charges and the like.


A recent survey conducted by the American Intellectual Property Law Association (“AIPLA”) reported that the median fees charged by law firms for preparing and filing original utility patent applications in 1999 ranged between $4,008 and $7,993, depending upon subject matter and complexity. See, American Intellectual Property Law Association, Report of Economic Survey, pp. 63-63 (1999). In addition, patent owners bring thousands of infringement suits each year in the federal courts. In the twelve months ending June 1998 a total of 1,996 patent-related cases were filed in the United States Federal District Courts. See, Annual Report of Judicial Statistics for 1997, Vol. 1, Civil Cases. The median cost of these suits in 1999 was estimated at $1.5 million per side through trial and appeal. It can be conservatively estimated that the total aggregate costs for obtaining, maintaining and enforcing patents in 1999 exceeded about $5.5 billion.


Because of the great importance of patents in the both the U.S. and global economies there has been continued interest in quantifying the value of patents and their contribution to economic prosperity of the individuals or companies that hold and/or control them. Such information can be useful for a variety of purposes. For example, patent holders themselves may be interested in using such information to help guide future decision-making or for purposes of tax treatment, transfer pricing or settlement of patent license disputes. Financial advisors and investors may seek to use such information for purposes of comparative value analysis and/or to construct measures of the “fundamental value” of publicly traded companies for purposes of evaluating possible strategic acquisitions or as a guide to investment. Economists may seek to use patent valuations for purposes of economic forecasting and planning. Insurance carriers may use such valuations to set insurance policy premiums and the like for insuring intangible assets. See, e.g., U.S. Pat. No. 6,018,714, incorporated herein by reference.


However, accurate valuing of patents and other intangible intellectual property assets is a highly difficult task requiring an understanding of a broad range of legal, technical and accounting disciplines. Intellectual property assets are rarely traded in open financial markets or sold at auction. They are intangible assets that secure unique benefits to the individuals or companies that hold them and/or exploit the underlying products or technology embodying the intellectual property. In the case of patent assets, for example, this unique value may manifest itself in higher profit margins for patented products, increased market power and/or enhanced image or reputation in the industry and/or among consumers or investors. These and other characteristics of intellectual property assets make such assets extremely difficult to value.


Patents derive unique value from the legal rights they secure, namely the right to exclude competition in the patented technology. This value (if any) usually manifests itself as a net increase in operating revenues resulting from either: (i) premium pricing of patented products or services; or (ii) royalty payments or other valuable consideration paid by competitors or other parties for use of the patented technology. Given these two inputs and the timing and probability of anticipated future revenue streams, an experienced valuation professional can readily estimate the value of a patent. See, Smith & Par, Valuation of Intellectual Property and Intangible Assets, 2nd Ed. (1989).


A familiar scenario is a patent licensed to a third party under an exclusive agreement that guarantees a predetermined income stream over a certain period of time. Using an income valuation approach, the intrinsic value of the licensed patent can be calculated simply as the net discounted present value of the future projected cash flows. Similarly, if the patent owner is exploiting the patented technology itself, the value of the patent may be fairly estimated as the net discounted present value of the incremental profit stream (assuming one can be identified) attributable to the patent over the remaining life of the patent or the economic life of the patented technology, whichever is shorter.


In these and similar scenarios where specific anticipated economic benefits can be identified and attributed to a particular intellectual property asset, accurate and credible estimations of value can be calculated using a traditional income valuation approach. In many cases, however, it is exceedingly difficult to identify with a desired degree of certainty a definite income stream or other anticipated economic benefit attributable to a particular intellectual property asset of interest. The classic example is a newly issued patent or an existing patent covering technology that, for whatever reason, has yet to be commercialized. In these and similar cases involving “unproven” patent assets the income valuation approach is less useful. The more tenuous the connection is between current revenues and anticipated future revenues, the more speculative the income valuation approach becomes.


For example, one popular approach involves guestimating “hypothetical” future license fees or royalties based on available data obtained from private license agreements and/or litigation settlements/awards involving patents in a similar technical field. While such analysis may be useful in certain cases, it suffers from several drawbacks that can lead to significant inaccuracies. One drawback is the inherent selection bias in the comparative data used to calculate hypothetical future license fees or royalties. By definition, all of the patents in the comparison group have been licensed, litigated and/or otherwise commercialized. This creates a “high-value” selection bias because most patents within the general population of patents are never licensed, litigated or commercialized at all. Thus, the approach will tend to over-value many patent assets. The approach also does not attempt to distinguish between similar patents based on underlying quality, breadth of claims, etc. Rather, the approach assumes that patents are fungible assets and that any one patent has essentially the same income earning potential as any other patent within the same field.


The reality is that every patent is unique. There are good patents and bad patents; broad patents and narrow patents; patents that are well-drafted and prosecuted and others that are not so well-drafted or prosecuted. Two patents in the same industry and relating to the same general subject matter can command drastically different royalty rates in a free market (or damage awards in litigation) depending upon subtle differences that affect the comparative breadth and defensibility of each patent.


Where there is enough money at stake, one or more patent lawyers can be engaged to analyze an individual patent and render a legal opinion, including an assessment of overall patent quality. But, such qualitative assessments are difficult to quantify in a way that lends itself to patent valuation analysis. Legal opinions are also inherently subjective, leaving the possibility for inconsistencies in assessed patent quality from attorney to attorney or from firm to firm.


What is needed is a purely objective approach for comparatively rating and valuing patents (particularly unproven patent assets) in a way that overcomes the above-noted problems and limitations.


SUMMARY OF THE INVENTION

Some embodiments of the invention include a method for constructing an estimated depreciation schedule for a patent. The steps for constructing this schedule may include: (1) determining a first function which approximately describes the rate of initial increase in expected forward patent citations over time; (2) determining a second function which approximately describes the rate of eventual decay in expected forward patent citations over time; and (3) constructing an estimated depreciation schedule using a calculated decay coefficient derived from said second function.


In some embodiments the first function comprises a lognormal probability distribution function. In some embodiments, the forward citation frequency is approximated by the product of the first and second functions.





BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments of the invention. Certain preferred embodiments and examples will now be described in detail having reference to the figures that follow, of which:



FIG. 1 is a graph that shows (the plot titled “Fwd Cite Rate,” marked with diamonds) the average age of patents receiving forward citations within a selected peer group (X-axis) and the relative frequency of forward citations being generated currently by newly issued patents (Y-axis). FIG. 1 also shows (the plot titled “Ramp Up & Decay”) the fitted approximation.



FIG. 2 is a graph that shows the product of the lognormal ramp-up function (the plot titled “Ramp Up,” marked with squares) and the exponential decay function (the plot titled “Decay,” marked with diamonds) to yield the fitted approximation in FIG. 1.



FIG. 3 is an example of the calculations for determining the parameters (or coefficients) best defining the functions shown in FIG. 2.





DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

In valuing a patent asset, a time-wise adjustment needs to be made in order to account for the effects of value depreciation over time. Depreciation of patent value over time may be caused by: (i) depletion of remaining patent term; and (ii) obsolescence over time of the underlying patented technology. A discussion on the valuation of patents can be found in U.S. Pat. No. 6,556,992, which is hereby incorporated herein by reference.


Term depletion depreciation accounts for loss of patent value due to exhausting the useful life of the patent. Every patent has a finite maximum term, averaging about 17.3 years. Depletion of patent term results in corresponding depletion of value because there is less time to extract the economic benefits of the patent and the underlying technology. This can generally be approximated as a straight line depreciation function over the expected patent term.


In addition to term depletion there is also loss of value due to age-related obsolescence of the underlying patented technology. In a licensing context, this can be thought of as time-wise diminishment or dilution of the “royalty base” caused by the introduction of new improvement patents that ultimately compete for the same royalty dollars. In some embodiments, the rate at which newer patents (and technology) replace older patents (and technology) is used as the rate of patent obsolescence.


In one embodiment, the average rate at which patents and technology become obsolete (e.g. replaced by newer technology and patents) assists in determining an appropriate patent depreciation schedule (i.e., how quickly a patent will lose value over time). Advantageously, the rate of patent obsolescence may also be used to formulate a patent filing and prosecution strategy and to set target benchmarks.


In some embodiments of the invention, the patent obsolescence rate is estimated by measuring the decline in the rate of forward citations of aging patents. As technological advances are made and as new patents are filed and issued, older patents gradually become less and less relevant to the newer patents that represent the latest, leading edge technology. This may be reflected by a declining rate of citations from newer patents to older patents. Eventually, older patents may cease receiving citations altogether as the older technologies gradually fade away and are replaced with newer technologies. This phenomenon is illustrated in FIG. 1 and FIG. 2.



FIG. 1 (plot titled “Fwd Cite Rate” marked with diamonds) shows the average age of patents receiving forward citations within a selected peer group (X-axis) and the relative frequency of forward citations being generated currently by newly issued patents (Y-axis). In some embodiments, the initial 4-5 year ramp-up of forward citations reflects the lag time for cited patents to be issued and for awareness to grow. In some embodiments, this initial ramp-up follows a lognormal probability distribution curve with awareness growing from 0% initially and asymptotically approaching 100% over time (plot titled “Ramp Up” and marked with squares, FIG. 2). In one embodiment the initial ramp up approximately follows a lognormal probability distribution curve defined by the following equation:

P(cite)=LOGNORMDIST(AGE,LOGMEAN,LOGSTDV)


Where:


P(cite)=probability of citation


AGE=patent age in years=int(PatentAge)+1 or “Age of Cited+1”


LOGMEAN=log mean of the lognormal distribution


LOGSTDV=log standard deviation of the lognormal distribution


In some embodiments of the invention, the decline in forward citation rates over time (the next 5-20 years) generally follows an exponential decay function. In some embodiments, FIG. 2 shows the exponential decay function as depicted by plot titled “Decay” and marked with diamonds. In one embodiment the decline in forward citation rates approximately follows an exponential decay function defined by the following equation:

FWDCITES(normalized)=STARTVALUE*EXP(AGE*DECAYRATE)


Where:


FWD CITES (normalized)=number of forward cites normalized as a percentage of the yearly maximum


STARTVALUE=hypothetical initial start value of FWD CITES (normalized)


DECAYRATE=decay coefficient of the exponential decay function


In some embodiments, the actual observed forward citation frequency is closely approximated by the product of the lognormal ramp-up function (plot titled “Ramp Up” and marked with squares, FIG. 2) and the exponential decay function (titled “Decay” and marked with diamonds, FIG. 2) to yield the fitted approximation (plot titled “Ramp Up & Decay”, FIG. 1). In some embodiments of the invention, the parameters (or coefficients) best defining each of these functions is iteratively determined to produce a best fit estimate of the observed citation frequency data. Preferably, the parameters (or coefficients) are solved or estimated simultaneously by using an iterative solver program such as the “Solver” add-on in the MS-Excel program.


One example of this calculation is provided in the table of FIG. 3.


The above calculation is merely an example and should not be construed to limit the scope of the invention. The square of the Pearson product moment correlation coefficient (“RSQ”) in this case was equal to 0.996, indicating that the model provided a very good fit, as illustrated in FIG. 1 (compare the overlapping plots of the “Fwd Cite Rate” marked with diamonds and the “Ramp up & Decay”).


In this case the exponent of the citation frequency decay function was iteratively determined to be −0.09954, indicating an average rate (or risk) of obsolescence of 9.95% per year. This corresponds to a technology half-life of 6.96 years. This suggests that the average utility or value of a patent selected from the peer group would decay to ½ of its original starting value after 6.96 years (ignoring the added effects of term depletion). In some embodiments, a technology's half-life is calculated by the following equation: HALFLIFE=ln(0.5)/DECAY RATE.


The data included in the above example can be further described as follows:
















Column
Explanation









Age of Cited
This is the approximate age in integer years




for each age-group of cited patents (Note:




AGE = Age of Cited + 1)



#Fwd Cites
This is the actual count of cited patents




for each age group



Fwd Cite Rate
This is the rate of cites normalized by




dividing #Fwd Cites by the Max(#Fwd Cites)



Ramp Up
This is the result of the ramp up function




p(cite) at the given AGE value



Decay
This is the result of the exponential decay




function at the given AGE value



Obsolescense
This is the product of (Ramp Up)*(Decay) -




essentially, this is the “Fwd Cite Rate”




as predicted by the model



Error{circumflex over ( )}2
This is the square of the actual Fwd Cite




Rate minus the predicted Fwd Cite Rate










The rate of patent obsolescence varies from technology to technology. Typically, faster-paced technologies, such as computer-electronics and software, decline more rapidly than slower-paced technologies, such as basic materials and simple mechanical technologies. Data on the obsolescence of a few exemplary US patent classifications resulting from one embodiment of the invention are provided below:


















Decay
Half-



Class
Description
Rate
Life
RSQ



















370
Multiplex communications
−26.8%
2.590
0.998


361
Electricity: electrical
−15.1%
4.610
0.997



systems . . .





556
Organic compounds . . .
−13.8%
5.030
0.981


237
Heating systems
−7.6%
9.090
0.95


181
Acoustics
−6.6%
10.480
0.976


28
Textiles: manufacturing
−5.4%
12.790
0.907


116
Signals and indicators
−4.8%
14.560
0.923


366
Agitating
−4.7%
14.670
0.98


105
Railway rolling stock
−4.5%
15.430
0.92









Some embodiments of the invention may provide all or some of the following advantages:

    • Provides an actual statistical measure of age-related obsolescence
    • Calculate patent depreciation schedules
    • Assess obsolecense “risk” for a single patent or a portfolio
    • Set target filing/prosecution rates to “replace” depleted patent assets


In some embodiments, it is contemplated that some or all of the steps described herein may be implemented within, or using, software modules (programs) that are executed by one or more general purpose computers. In these embodiments, the software modules may be stored on or within any suitable computer-readable medium. It should be understood that the various steps may alternatively be implemented in-whole or in-part within specially designed hardware.


Although this invention has been disclosed in the context of certain preferred embodiments and examples, it will be understood by those skilled in the art that the present invention extends beyond the specifically disclosed embodiments to other alternative embodiments and/or uses of the invention and obvious modifications and equivalents thereof. Thus, it is intended that the scope of the present invention herein disclosed should not be limited by the particular disclosed embodiments described above.

Claims
  • 1. A computer implemented method for generating a predicted depreciation schedule for a set of one or more patents, the computer implemented method comprising: selecting via a computer system a set of one or more patents for which to generate the predicted depreciation schedule;accessing via the computer system, from a data repository, forward citation frequency data related to the set of one or more patents;generating via the computer system a lognormal probability distribution function for the set of one or more patents based at least partially on the forward citation frequency data for a selected period of time, the lognormal probability distribution function representing ramp up public awareness of the set of one or more patents, wherein the lognormal probability distribution function is iteratively generated to determine coefficients that produce a best fit estimate of the forward citation frequency data;generating via the computer system an exponential decay function for the set of one or more patents based at least partially on the forward citation frequency data for a second period of time that is beyond the selected period of time, the exponential decay function representing obsolescence of the set of one or more patents, wherein the exponential decay function is iteratively generated to determine coefficients that produce a best fit estimate of the forward citation frequency data; andstoring via the computer system the predicted depreciation schedule for the set of one or more patents, the predicted depreciation schedule comprising a combination of the lognormal probability distribution function and the exponential decay function, wherein the predicted depreciation schedule is correlated to a depreciation rate for the set of one or more patents.
  • 2. The computer implemented method of claim 1, further comprising electronically determining whether to pay a patent maintenance fee for a selected patent based at least partially on determining the depreciation rate from the predicted depreciation schedule of the set of one or more patents for a selected time period.
  • 3. A computer implemented method for constructing an estimated depreciation schedule for a patent, comprising: electronically selecting a patent to generate the estimated depreciation schedule statistically correlated to expected forward citations to the patent;electronically determining a first function which approximately describes a rate of initial increase in expected forward patent citations over time;electronically determining a second function which approximately describes a rate of eventual decay in expected forward patent citations over time, the second function representing obsolescence of the patent;electronically constructing the estimated depreciation schedule using a calculated decay coefficient derived from said first and second functions, wherein the estimated depreciation schedule is constructed by a computer system; andelectronically storing in a data repository the estimated depreciation schedule.
  • 4. The method of claim 3 wherein said first function comprises a lognormal probability distribution function.
  • 5. A computer implemented method for generating a predicted depreciation schedule for a set of one or more unproven patent assets, the computer implemented method comprising: selecting via a computer system a set of one or more patents for which to generate the predicted depreciation schedule;accessing via the computer system, from a data repository, forward citation frequency data related to the set of one or more patents, wherein the forward citation frequency data includes frequency of citations of the set of one or more patents by new patents;generating via the computer system a lognormal probability distribution function for the set of one or more patents based at least partially on the forward citation frequency data for a selected period of time, the lognormal probability distribution function representing ramp up public awareness of the set of one or more patents, wherein the lognormal probability distribution function is iteratively generated to determine coefficients that produce a best fit estimate of the forward citation frequency data;generating via the computer system an exponential decay function for the set of one or more patents based at least partially on the forward citation frequency data for a second period of time that is beyond the selected period of time, the exponential decay function representing obsolescence of the set of one or more patents, wherein the exponential decay function is iteratively generated to determine coefficients that produce a best fit estimate of the forward citation frequency data; andstoring via the computer system the predicted depreciation schedule for the set of one or more patents, the predicted depreciation schedule comprising a combination of the lognormal probability distribution function and the exponential decay function, wherein the predicted depreciation schedule is correlated to a depreciation rate for the set of one or more patents.
  • 6. The method of claim 1, further comprising formulating patent filing and prosecution strategy based at least on the exponential decay function.
  • 7. The method of claim 3, further comprising formulating patent filing and prosecution strategy based at least on the exponential decay function.
  • 8. The method of claim 5, further comprising formulating patent filing and prosecution strategy based at least on the exponential decay function.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 60/714,713, filed Sep. 7, 2005, the entirety of which is hereby incorporated by reference

US Referenced Citations (141)
Number Name Date Kind
4991087 Burkowski et al. Feb 1991 A
5175681 Iwai et al. Dec 1992 A
5392390 Crozier Feb 1995 A
5544302 Nguyen Aug 1996 A
5546529 Bowers et al. Aug 1996 A
5576954 Driscoll Nov 1996 A
5594897 Goffman Jan 1997 A
5608620 Lundgren Mar 1997 A
5615362 Jensen et al. Mar 1997 A
5625814 Luciw Apr 1997 A
5640553 Schultz Jun 1997 A
5642502 Driscoll Jun 1997 A
5680305 Apgar, IV Oct 1997 A
5694592 Driscoll Dec 1997 A
5721903 Anand Feb 1998 A
5754840 Rivette et al. May 1998 A
5764058 Itskovich et al. Jun 1998 A
5774833 Newman Jun 1998 A
5778362 Deerwester Jul 1998 A
5781773 Vanderpool et al. Jul 1998 A
5794236 Mehrle Aug 1998 A
5799325 Rivette et al. Aug 1998 A
5802501 Graff Sep 1998 A
5808615 Hill et al. Sep 1998 A
5848409 Ahn Dec 1998 A
5893092 Driscoll Apr 1999 A
5926811 Miller et al. Jul 1999 A
5930784 Hendrickson Jul 1999 A
5937402 Pandit Aug 1999 A
5991751 Rivette et al. Nov 1999 A
5999907 Donner Dec 1999 A
6009436 Motoyama et al. Dec 1999 A
6014663 Rivette et al. Jan 2000 A
6018714 Risen, Jr. et al. Jan 2000 A
6018749 Rivette et al. Jan 2000 A
6038561 Snyder et al. Mar 2000 A
6038574 Pitkow et al. Mar 2000 A
6049811 Petruzzi et al. Apr 2000 A
6088692 Driscoll Jul 2000 A
6108651 Guha Aug 2000 A
6154725 Donner Nov 2000 A
6175824 Breitzman et al. Jan 2001 B1
6182091 Pitkow et al. Jan 2001 B1
6202058 Rose et al. Mar 2001 B1
6212530 Kadlec Apr 2001 B1
6216134 Heckerman et al. Apr 2001 B1
6263314 Donner Jul 2001 B1
6285999 Page Sep 2001 B1
6286018 Pitkow et al. Sep 2001 B1
6289342 Lawrence et al. Sep 2001 B1
6298327 Hunter et al. Oct 2001 B1
6330547 Martin Dec 2001 B1
6339767 Rivette et al. Jan 2002 B1
6363373 Steinkraus Mar 2002 B1
6389418 Boyack et al. May 2002 B1
6389436 Chalrabarti et al. May 2002 B1
6421066 Sivan Jul 2002 B1
6452613 Lefebvre et al. Sep 2002 B1
6453315 Weissman et al. Sep 2002 B1
6457028 Pitkow et al. Sep 2002 B1
6463431 Schmitt Oct 2002 B1
6490548 Engel Dec 2002 B1
6526440 Bharat Feb 2003 B1
6556992 Barney et al. Apr 2003 B1
6560600 Broder May 2003 B1
6571241 Nosohara May 2003 B1
6574632 Fox et al. Jun 2003 B2
6587850 Zhai Jul 2003 B2
6591261 Arthurs Jul 2003 B1
6654767 McAnaney et al. Nov 2003 B2
6662178 Lee Dec 2003 B2
6665656 Carter Dec 2003 B1
6665670 Winer et al. Dec 2003 B2
6694331 Lee Feb 2004 B2
6751613 Lee et al. Jun 2004 B1
6754873 Law et al. Jun 2004 B1
6829603 Chai et al. Dec 2004 B1
6832211 Thomas et al. Dec 2004 B1
6862579 Mathews et al. Mar 2005 B2
6879990 Boyer et al. Apr 2005 B1
6940509 Crow et al. Sep 2005 B1
6996273 Mihcak et al. Feb 2006 B2
7054856 Won et al. May 2006 B2
7089192 Bracchita et al. Aug 2006 B2
7092961 Minezaki et al. Aug 2006 B2
7099876 Hetherington et al. Aug 2006 B1
7106329 Miller et al. Sep 2006 B1
7111002 Zhang et al. Sep 2006 B2
7188069 Hagelin Mar 2007 B2
7194490 Zee Mar 2007 B2
7216100 Elliot May 2007 B2
7228288 Elliot Jun 2007 B2
7242217 Van Wageningen et al. Jul 2007 B2
7269566 Elliott Sep 2007 B2
7292994 Prokoski Nov 2007 B2
7320000 Chitrapura Jan 2008 B2
7331016 Williams et al. Feb 2008 B2
7433884 Breitzman Oct 2008 B2
7536312 Block May 2009 B2
7546265 Donner Jun 2009 B1
7558749 Chen Jul 2009 B2
7606757 Poltorak Oct 2009 B1
20020002524 Kossovsky et al. Jan 2002 A1
20020004775 Kossovsky et al. Jan 2002 A1
20020022974 Lindh Feb 2002 A1
20020035499 Germeraad et al. Mar 2002 A1
20020046038 Prokoski Apr 2002 A1
20020077835 Hagelin Jun 2002 A1
20020082778 Barnett et al. Jun 2002 A1
20020087442 Reader Jul 2002 A1
20020099637 Wilkinson et al. Jul 2002 A1
20020099638 Coffman et al. Jul 2002 A1
20030036945 Del Vecchio et al. Feb 2003 A1
20030065658 Matsubayashi et al. Apr 2003 A1
20030078870 Datar et al. Apr 2003 A1
20030126054 Purcell, Jr. Jul 2003 A1
20030212572 Poltorak Nov 2003 A1
20030217113 Katz et al. Nov 2003 A1
20040010393 Barney Jan 2004 A1
20040103112 Colson et al. May 2004 A1
20050021434 D'Loren Jan 2005 A1
20050071174 Leibowitz et al. Mar 2005 A1
20050083850 Sin et al. Apr 2005 A1
20050149420 Hagelin Jul 2005 A1
20060036452 Williams Feb 2006 A1
20060036453 Williams Feb 2006 A1
20060036529 Williams Feb 2006 A1
20060036632 Williams Feb 2006 A1
20060036635 Williams Feb 2006 A1
20060074867 Breitzman Apr 2006 A1
20060122849 Masuyama et al. Jun 2006 A1
20060218056 Dickman Sep 2006 A1
20060224972 Albrecht et al. Oct 2006 A1
20070073625 Shelton Mar 2007 A1
20070073748 Barney Mar 2007 A1
20070088738 Barney et al. Apr 2007 A1
20070094297 Barney Apr 2007 A1
20070150298 Barney Jun 2007 A1
20070208669 Rivette et al. Sep 2007 A1
20080091620 Vollenweider et al. Apr 2008 A1
20080147541 Jones Jun 2008 A1
Foreign Referenced Citations (3)
Number Date Country
1 215 599 Jun 2002 EP
WO 0075851 Dec 2000 WO
WO 0135277 May 2001 WO
Related Publications (1)
Number Date Country
20070094297 A1 Apr 2007 US
Provisional Applications (1)
Number Date Country
60714713 Sep 2005 US