prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What type of distribution do the tail distributions of price returns follow?

Read the following text and answer briefly.

Context 1: Paper Info

Title: Age and market capitalization drive large price variations of cryptocurrencies
Publish Date: 23 Feb 2023
Author List: 

Figure

Figure 3  Illustration of different effects of age and market capitalization on power-law exponents of cryptocurrencies (a) Posterior probability distributions of the linear coefficients associated with the effects of age [p(A)] and (b) the effects of market capitalization [p(C)] on power-law exponents related to large positive returns Panels (c) and (d) show the analogous distributions for the association with power-law exponents related to large negative returns In all panels, the different curves show the distributions for each of the top 20 cryptoassets by market capitalization Cryptocurrencies significantly affected by age or market capitalization are highlighted in boldface, and the numbers between brackets show their positions in the market capitalization rank 
Figure S5 There is more probability mass in the positive tail than in the negative tail of price returns (a) Probability distributions of the lower cut-offs (r min ) obtained by applying the Clauset-Shalizi-Newman method to positive (blue) and negative (red) returns The vertical dashed lines indicate the median values of r min for positive and negative returns (b) Probability distributions of 90th percentiles (r 90 ) estimated from the power-law models adjusted to positive (blue) and negative (red) returns The vertical dashed lines indicate the median values of r 90 for positive and negative returns (c) Probability distributions of the fraction of weeks that r 90 estimated from positive returns (r + 90 ) is larger than r 90 estimated from negative returns (r − 90 ) This fraction is calculated only for weeks in which the power-law hypothesis is not rejected for both tails The percentage of cryptoassets for which r + 90 > r − 90 is shown in the panels The first column of panels depicts the results when considering data from all cryptocurrencies, while the second and third columns present the results for the top 2000 and top 200 cryptocurrencies by market capitalization, respectively 
Figure S7 Robustness of the results of Fig  2(b)-(d) against considering only cryptocurrencies with fraction of rejection f r < 0 1 Panels (a) and (b) show the same distributions of Fig  S4 but after filtering out all time series of cryptocurrencies with fraction of rejections f r ≥ 0 1 As in the case related to sampling issues, we observe that these distributions barely change when considering only cryptocurrencies with f r < 0 1 Indeed, the distributions in this figure are not significantly distinguishable from their counterparts in Fig  S4 (two-sample Kolmogorov-Smirnov test, p > 0 05) 

abstract

Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions  This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns 
However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization  Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market 
We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them  Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones 
Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities  Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28% of the cryptocurrencies as they age and grow in market capitalization 
Since the creation of Bitcoin in 2008 , various different cryptoassets have been developed and are now considered to be at the cutting edge of innovation in finance   These digital financial assets are vastly diverse in design characteristics and intended purposes, ranging from peer-to-peer networks with underlying cash-like digital currencies (e g 
Bitcoin) to general-purpose blockchains transacting in commodity-like digital assets (e g  Ethereum), and even to cryptoassets that intend to replicate the price of conventional assets such as the US dollar or gold (e g  Tether and Tether Gold)   With more than nine thousand cryptoassets as of 2022 , the total market value of cryptocurrencies has grown massively to a staggering $2 trillion peak in 2021  
Despite long-standing debates over the intrinsic value and legality of cryptoassets , or perhaps even precisely due to such controversies, it is undeniable that cryptocurrencies are increasingly attracting the attention of academics, investors, and central banks, around the world   Moreover, these digital assets have been at the forefront of sizable financial gains and losses in recent years , they have been recognized as the main drivers of the brand-new phenomena of cryptoart and NFTs , but also as facilitators of illegal activities, such as money laundering and dark trade  
Financial research dedicated Our results are based on daily price time series of 7111 cryptocurrencies that comprise a significant part of all currently available cryptoassets (see Methods for details)  From these price series, we have estimated their logarithmic returns 2/16 Log-return, r )  The black horizontal arrow represents a given position of the expanding time window (at t = 2004 days) used to sample the return series over the entire history of Bitcoin 
This time window expands in weekly steps (seven time series observations), and for each position, we separate the positive (blue) from the negative (red) price returns  The gray line illustrates observations that will be included in future positions of the expanding time window (t > 2004)  (b) Survival functions or the complementary cumulative distributions of positive (blue) and negative (red) price returns within the expanding time window for t = 2004 days and above the lower bound of the power-law regime estimated from the Clauset-Shalizi-Newman method  
The dashed lines show the adjusted power-law functions, p(r) ∼ r −α , with α = 4 5 for positive returns and α = 3 0 for negative returns  (c) Time series of the power-law exponents α t for the positive (blue) and negative (red) return distributions obtained by expanding the time window from the hundredth observation (t = 100) to the latest available price return of Bitcoin 
The circular markers represent the values for the window position at t = 2004 days and the dashed lines indicate the median of the power-law exponents ( α+ = 4 50 for positive returns and α− = 2 99 for negative returns)  (d) Time series of the p-values related to the power-law hypothesis of positive (blue) and negative (red) price returns for every position of the expanding time window 
The dashed line indicates the threshold (p = 0 1) above which the power-law hypothesis cannot be rejected  For Bitcoin, the power-law hypothesis is never rejected for positive returns (fraction of rejection f r = 0) and rejected in only 4% of the expanding time window positions (fraction of rejection f r = 0 04) 
where x t represents the price of a given cryptocurrency at day t  All return time series in our analysis have at least 200 observations (see Supplementary Figure for the length distribution)  Figure (a) illustrates Bitcoin's series of daily returns  To investigate whether and how returns have changed over the aging and growing processes of all cryptocurrencies, we sample all time series of log-returns using a time window that expands in weekly steps (seven time series observations), starting from the hundredth observation to the latest return observation 
In each step, we separate the positive from the negative return values and estimate their power-law behavior using the Clauset-Shalizi-Newman method   Figure (a) further illustrates this procedure, where the vertical dashed line represents a given position of the time window (t = 2004 days), the blue and red lines indicate positive and negative returns, respectively, and the gray lines show the return observations that will be included in the expanding time window in future steps 
Moreover, Fig  (b) shows the corresponding survival functions (or complementary cumulative distributions) for the positive (blue) and negative (red) returns of Bitcoin within the time window highlighted in Fig  (a)  These survival functions correspond to return values above the lower bound of the power-law regime (r min ) and dashed lines in Fig  (b) show the power-law functions adjusted to data, that is,
with α = 4 5 for the positive returns and α = 3 0 for the negative returns in this particular position of the time window (t = 2004 days)  We have further verified the goodness of the power-law fits using the approach proposed by Clauset et al  (see also Preis et al  )  As detailed in the Methods section, this approach consists in generating several synthetic samples under the power-law hypothesis, adjusting these simulated samples, and estimating the fraction of times the Kolmogorov-Smirnov distance between the adjusted power-law and the synthetic samples is larger than the value calculated from the empirical data 
This fraction defines a p-value and allows us to reject or not the power-law hypothesis of the return distributions under a given confidence level  Following Refs  we consider the more conservative 90% confidence level (instead of the more lenient and commonly used 95% confidence level), rejecting the power-law hypothesis when p-value ≤ 0 1 
For the particular examples in Fig  (b), the p-values are respectively 1 00 and 0 17 for the positive and negative returns, and thus we cannot reject the power-law hypotheses  After sampling the entire price return series, we obtain time series for the power-law exponents (α t ) associated with positive and negative returns as well as the corresponding p-values time series for each step t of the expanding time window 
These time series allow us to reconstruct the aging process of the return distributions over the entire history of each cryptoasset and probe possible time-dependent patterns  Figures ) and 1(d) show the power-law exponents and p-values time series for the case of Bitcoin  The power-law hypothesis is never rejected for positive returns and rarely rejected for negative returns (about 4% of times) 
Moreover, the power-law exponents exhibit large fluctuations at the beginning of the time series and become more stable as Bitcoin matures as a financial asset (a similar tendency as reported by Begušić et al  )  The time evolution of these exponents further shows that the asymmetry between positive and negative returns observed in Fig  ) is not an incidental feature of a particular moment in Bitcoin's history 
Indeed, the power-law exponent for positive returns is almost always larger than the exponent for negative returns, implying that large negative price returns have been more likely to occur than their positive counterparts over nearly the entire history of Bitcoin covered by our data  However, while the difference between positive and negative exponents has approached a constant value, both exponents exhibit an increasing trend, indicating that large price variations are becoming less frequent with the coming-of-age of Bitcoin 
The previous analysis motivates us to ask whether the entire cryptocurrency market behaves similarly to Bitcoin and what other common patterns digital currencies tend to follow  To start answering this question, we have considered the p-values series of all cryptocurrencies to verify if the power-law hypothesis holds in general 
Figure (a) shows the percentage of cryptoassets rejecting the power-law hypothesis in at most a given fraction of the weekly positions of the expanding time window ( f r )  Remarkably, the hypothesis that large price movements (positive or negative) follow a power-law distribution is never rejected over the entire history of about 70% of all digital currencies in our dataset 
This analysis also shows that only ≈2% of cryptocurrencies reject the power-law hypothesis in more than half of the positions of the expanding time window ( f r ≥ 0 5)  For instance, considering a 10% threshold as a criterion ( f r ≤ 0 1), we find that about 85% of cryptocurrencies have return distributions adequately modeled by power laws 
Increasing this threshold to a more lenient 20% threshold ( f r ≤ 0 2), we find large price movements to be power-law distributed for about 91% of cryptocurrencies  These results thus provide strong evidence that cryptoassets, fairly generally, present large price movements quite well described by power-law distributions 
Moreover, this conclusion is robust when starting the expanding window with a greater   Large price movements are power-law distributed over the entire history of most cryptocurrencies with median values typically smaller than those found for traditional assets  (a) Percentage of cryptoassets rejecting the power-law hypothesis for large positive (blue) or negative (red) price returns in at most a given fraction of the weekly positions of the expanding time window ( f r ) used to sample the return series 
Remarkably, 68% of all 7111 digital currencies are compatible with the power-law hypothesis over their entire history, and about 91% of them reject the power-law hypothesis in less than 20% of the positions of the expanding time window ( f r ≤ 0 2)  (b) Probability distributions obtained via kernel density estimation of the median values of the power-law exponents along the history of each digital currency 
The blue curve shows the distribution of the median exponents related to positive returns ( α+ ) and the red curve does the same for negative returns ( α− )  The medians of α+ and α− are indicated by vertical dashed lines  Panels (c) and (d) show the distributions of these median exponents when considering the top 2000 and the top 200 cryptocurrencies by market capitalization, respectively 
We observe that the distributions of α+ and α− tend to shift toward larger values when considering the largest cryptoassets  number of return observations (between 100 and 300 days) and filtering out cryptoassets with missing observations (Supplementary Figures )  Still, it is worth noticing the existence of a few cryptoassets (9 of them) with relatively small market capitalization (ranking below the top 1000) for which the power-law hypothesis is always rejected (Supplementary Table ) 
Having verified that large price movements in the cryptocurrency market are generally well-described by powerlaw distributions, we now focus on the power-law exponents that typically characterize each cryptoasset  To do so, we select all exponent estimates over the entire history of each digital asset for which the power-law hypothesis is not rejected and calculate their median values for both the positive ( α+ ) and negative ( α− ) returns 
The dashed lines in Fig  ) show these median values for Bitcoin where α+ = 4 50 and α− = 2 99  It is worth noticing that the variance of large price movements σ 2 is finite only for α > 3, as the integral σ 2 ∼ ∞ r min r 2 p(r)dr diverges outside this interval  Thus, while the typical variance of large positive returns is finite for Bitcoin, negative returns are at the limit of not having a typical scale and are thus susceptible to much larger variations 
Figure shows the probability distribution for the median power-law exponents of all cryptoassets grouped by large positive and negative returns  We note that the distribution of typical power-law exponents associated with large positive returns is shifted to smaller values when compared with the distribution of exponents related to large negative returns 
The medians of these typical exponents are respectively 2 78 and 3 11 for positive and negative returns  This result suggests that the asymmetry in large price movements we have observed for Bitcoin is an overall feature of the cryptocurrency market  By calculating the difference between the typical exponents related to positive and negative large returns (∆α = α+ − α− ) for each digital currency, we find that about 2/3 of cryptocurrencies have α+ < α− (see Supplementary Figure for the probability distribution of ∆α) 
Thus, unlike Bitcoin, most cryptocurrencies have been more susceptible to large positive price variations than negative ones  While this asymmetry in the return distributions indicates that extremely large price variations tend to be positive, it does not necessarily imply positive price variations are more common for any threshold in the return values 
This happens because the fraction of events in each tail is also related to the lower bound of the power-law regime (r min )  However, we have found the distribution of r min to be similar among the positive and negative returns [Supplementary Figure ]  The distribution of high percentile scores (such as the 90th percentile) is also shifted to larger values for positive returns [Supplementary Figure ] 
Moreover, this asymmetry in high percentile scores related to positive and negative returns is systematic along the evolution of the power-law exponents [Supplementary Figure ]  These results thus indicate that there is indeed more probability mass in the positive tails than in the negative ones, a feature that likely reflects the current expansion of the cryptocurrency market as a whole 
The distributions in Fig  ) also show that large price variations do not have a finite variance for a significant part of cryptoassets, that is, α+ ≤ 3 for 62% of cryptocurrencies and α− ≤ 3 for 44% of cryptocurrencies  A significant part of the cryptocurrency market is thus prone to price variations with no typical scale 
Intriguingly, we further note the existence of a minority group of cryptoassets with α+ ≤ 2 (7%) or α− ≤ 2 (3%)  These cryptocurrencies, whose representative members are Counos X (CCXX, rank 216) with α − = 1 96 and α + = 1 84 and Chainbing (CBG, rank 236) with α + = 1 87, are even more susceptible to extreme price variations as one cannot even define the average value µ for large price returns, as the integral µ ∼ ∞ r min rp(r)dr diverges for α ≤ 2  We have also replicated the previous analysis when considering cryptocurrencies in the top 2000 and top 200 rankings of market capitalization (as of July 2022) 
Figures ) and 2(d) show the probability distribution for the median power-law exponents of these two groups  We observe that these distributions are more localized (particularly for the top 200) than the equivalent distributions for all cryptocurrencies  The fraction of cryptocurrencies with no typical scale for large price returns ( α+ ≤ 3 and α− ≤ 3) is significantly lower in these two groups compared to all cryptocurrencies 
In the top 2000 cryptocurrencies, 51% have α+ ≤ 3 and 26% have α− ≤ 3  These fractions are even smaller among the top 200 cryptocurrencies, with only 44% and 15% not presenting a typical scale for large positive and negative price returns, respectively  We further observe a decrease in the fraction of cryptoassets for which the average value for large price returns is not even finite, as only 2% and 1% of top 2000 cryptoassets have α+ ≤ 2 and α− ≤ 2  This reduction is more impressive among the top 200 cryptocurrencies as only the cryptoasset Fei USD (FEI, rank 78) has α+ = 1 97 and none is characterized by α− ≤ 2  The medians of α+ and α− also increase from 2 78 and 3 11 for all cryptocurrencies to 2 98 and 3 35 for the top 2000 and to 3 08 and 3 58 for the top 200 cryptocurrencies 
Conversely, the asymmetry between positive and negative large price returns does not differ much among the three groups, with the condition α+ < α− holding only for a slightly larger fraction of top 2000 (69 1%) and top 200 (70 6%) cryptoassets compared to all cryptocurrencies (66 4%)  Moreover, all these patterns are robust when filtering out time series with sampling issues or when considering only cryptoassets that stay compatible with the power-law hypothesis in more than 90% of the positions of the expanding time window (Supplementary Figures ) 
We also investigate whether the patterns related to the median of the power-law exponents differ among groups of cryptocurrencies with different designs and purposes  To do so, we group digital assets using the 50 most common tags in our dataset (e g  "bnb-chain", "defi", and "collectibles-nfts") and estimate the probability distributions of the median exponents α+ and α− (Supplementary Figures ) 
These results show that design and purpose affect the dynamics of large price variations in the cryptocurrency market as the medians of typical exponents range from 2 4 to 3 7 among the groups  The lowest values occur for cryptocurrencies tagged as "doggone-doggerel" (medians of α+ and α− are 2 38 and 2 83), "memes" (2 41 and 2 87), and "stablecoin" (2 65 and 2 79) 
Digital currencies belonging to the first two tags overlap a lot and have Dogecoin (DOGE, rank 9) and Shiba Inu (SHIB, rank 13) as the most important representatives  Cryptoassets with these tags usually have humorous characteristics (such as an Internet meme) and several have been considered as a form of pump-and-dump scheme , a type of financial fraud in which false statements artificially inflate asset prices so the scheme operators sell their overvalued cryptoassets 
Conversely, cryptoassets tagged as "stablecoin" represent a class of cryptocurrencies designed to have a fixed exchange rate to a reference asset (such as a national currency or precious metal)   While the price of stablecoins tends to stay around the target values, their price series are also marked by sharp variations, which in turn are responsible for their typically small power-law exponents 
This type of cryptoasset has been shown to be prone to failures , such as the recent examples of TerraUSD (UST) and Tron's USDD (USDD) that lost their pegs to the US Dollar producing large variations in their price series  The asymmetry between positive and negative large returns also emerges when grouping the cryptocurrencies using their tags 
All 50 tags have distributions of α+ shifted to smaller values when compared with the distributions of α− , with differences between their medians ranging from −0 74 ("okex-blockdream-ventures-portfolio") to −0 14 ("stablecoin")  Indeed, only four ('stablecoin", "scrypt", "fantom-ecosystem" and "alameda-research-portfolio") out of the fifty groupings have both distributions indistinguishable under a two-sample Kolmogorov-Smirnov test (p-value > 0 05) 
Focusing now on the evolution of the power-law exponents quantified by the time series α t for positive and negative returns, we ask whether these exponents present particular time trends  For Bitcoin [Fig  )], α t seems to increase with time for both positive and negative returns  At the same time, the results of Fig  also suggest that market capitalization affects these power-law exponents 
To verify these possibilities, we assume the power-law exponents (α t ) to be linearly associated with the cryptocurrency's age (y t , measured in years) and the logarithm of market capitalization (log c t )  As detailed in the Methods section, we frame this problem using a hierarchical Bayesian model 
This approach assumes that the linear coefficients associated with the effects of age (A) and market capitalization (C) of each digital currency are drawn from distributions with means µ A and µ C and standard deviations σ A and σ C , which are in turn distributed according to global distributions representing the overall impact of these quantities on the cryptocurrency market 
The Bayesian inference process consists of estimating the posterior probability distributions of the linear coefficients for each cryptocurrency as well as the posterior distributions of µ A , µ C , σ A , and σ C , allowing us to simultaneously probe asset-specific tendencies and overall market characteristics 
Moreover, we restrict this analysis to the 2140 digital currencies having more than 50 observations of market capitalization concomitantly to the time series of the power-law exponents in order to have enough data points for detecting possible trends  When considering the overall market characteristics, we find that the 94% highest density intervals for µ A ([-0 01, 0 06] for positive and [-0 02, 0 03] for negative returns) and µ C ([-0 02, 0 03] for positive and [-0 001, 0 04] for negative returns) include the zero (see Supplementary Figure for their distributions) 
Thus, there is no evidence of a unique overall pattern for the association between the power-law exponents and age or market capitalization followed by a significant part of the cryptocurrency market  Indeed, the 94% highest density intervals for σ A ([0 87, 0 93] for positive and [0 63, 0 70] for negative returns) and σ C ([0 57, 0 61] for positive and [0 49, 0 52] for negative returns) indicate that the cryptocurrency market is highly heterogeneous regarding the evolution of power-law exponents associated with large price variations (see Supplementary Figure for the distributions of σ A and σ C )  Figure illustrates these heterogeneous behaviors by plotting the posterior probability distributions for the linear coefficients associated with the effects of age (A) and market capitalization (C) for the top 20 digital assets, where cryptocurrencies which are significantly affected (that is, the 94% highest density intervals for A or C do not include the zero) by these quantities are highlighted in boldface 
Even this small selection of digital  currencies already presents a myriad of patterns  First, we observe that the power-law exponents of a few top 20 cryptocurrencies are neither correlated with age nor market capitalization  That is the case of Shiba Inu (SHIB, rank 13) and Dai (DAI, rank 11) for both positive and negative returns, UNUS SED LEO (LEO, rank 18) and Polkadot (DOT, rank 12) for the positive returns, and USDCoin (USDC, rank 4) and Solana (SOL, rank 9) for negative returns 
There are also cryptocurrencies with exponents positively or negatively correlated only with market capitalization  Examples include Tether (USDT, rank 3) and Dogecoin (DOGE, rank 10), for which the power-law exponents associated with positive returns increase with market capitalization, and Binance USD (BUSD, rank 6), for which power-law exponents associated with positive and negative returns decrease with market capitalization 
We also observe cryptocurrencies for which age and market capitalization simultaneously affect the power-law exponents  Polygon (MATIC, rank 14) is an example where the power-law exponents associated with positive returns tend to increase with age and decrease with market capitalization  Finally, there are also cryptocurrencies with power-law exponents only associated with age 
That is the case of Bitcoin (BTC, rank 1), Ethereum (ETH, rank 2), and Cardano (ADA, rank 8), for which the power-law exponents related to positive and negative returns increase with age, but also the case of Uniswap (UNI, rank 19), for which the exponents decrease with age  Figure systematically extends the observations made for the top 20 cryptoassets to all 2140 digital currencies for which we have modeled the changes in the power-law exponents as a function of age and market capitalization 
First, we note that only 10% of cryptocurrencies have power-law exponents not significantly affected by age and market capitalization  The vast majority (90%) displays some relationship with these quantities  However, these associations are as varied as the ones we have observed for the top 20 cryptoassets 
About 52% of cryptocurrencies have power-law exponents simultaneously affected by age and market capitalization  In this group, these quantities simultaneously impact the exponents related to positive and negative returns of 34% of cryptoassets, whereas the remainder is affected only in the positive tail (9%) or only in the negative tail (9%) 
Moving back in the hierarchy, we find that the power-law exponents of 32% of cryptocurrencies are affected only by age while a much minor fraction (6%) is affected only by market capitalization  Within the group only affected by age, we observe that the effects are slightly more frequent only on the exponents related to negative returns (12%), compared to cases where effects are restricted only to positive returns (10%) or simultaneously affect both tails (10%) 
Finally, within the minor group only affected by market capitalization, we note that associations more frequently involve only exponents related to negative returns (3%) compared to the other two cases (2% only positive returns and 1% for both positive and negative returns)  Beyond the previous discussion about whether positive or negative returns are simultaneously or individually affected by age and market capitalization, we have also categorized the direction of the trend imposed by these two quantities on the power-law exponents 
Blue rectangles in Fig  represent the fraction of relationships for which increasing age or market capitalization (or both) is associated with a raise in the power-law exponents  About 28% of all cryptocurrencies exhibit this pattern in which large price variations are expected to occur less frequently as they grow and age 
Conversely, the red rectangles in Fig  depict the fraction of relationships for which increasing age or market capitalization (or both) is associated with a reduction in the power-law exponents  This case comprises about 25% of all cryptocurrencies for which large price variations are likely to become more frequent as they grow in market capitalization and age 
Still, the majority of associations represented by green rectangles refer to the case where the effects of age and market capitalization point in different directions (e g  exponents increasing with age while decreasing with market capitalization)  About 36% of cryptocurrencies fit this condition which in turn contributes to consolidating the cumbersome hierarchical structure of patterns displayed by cryptocurrencies regarding the dynamics of large price variations 
This complex picture is not much different when considering only cryptocurrencies in the top 200 market capitalization rank (Supplementary Figure )  However, we do observe an increased prevalence of patterns characterized by exponents that rise with age and market capitalization (37%), suggesting that large price variations are becoming less frequent among the top 200 cryptocurrencies than in the overall market 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 36% of the associations are classified as mixed trends (green rectangles), 28% are increasing trends (blue rectangles), and 26% are decreasing trends (red rectangles)  We have studied the distributions of large price variations of a significant part of the digital assets that currently comprise the entirety of the cryptocurrency market 
Unlike previous work, we have estimated these distributions for entire historical price records of each digital currency, and we have identified the patterns under which the return distributions change as cryptoassets age and grow in market capitalization  Similarly to conventional financial assets , our findings show that the return distributions of the vast majority of cryptoassets have tails that are described well by power-law functions along their entire history 
The typical power-law exponents of cryptocurrencies (α ∼ 3) are, however, significantly smaller than those reported for conventional assets (α ∼ 4)   This feature corroborates the widespread belief that cryptoassets are indeed considerably more risky for investments than stocks or other more traditional financial assets 
Indeed, we have found that about half of the cryptocurrencies in our analysis do not have a characteristic scale for price variations, and are thus prone to much higher price variations than those typically observed in stock markets  On the upside, we have also identified an asymmetry in the power-law exponents for positive and negative returns in about 2/3 of all considered cryptocurrencies, such that these exponents are smaller for positive than they are for negative returns 
This means that sizable positive price variations have generally been more likely to occur than equally sizable negative price variations, which in turn may also reflect the recent overall expansion of the cryptocurrency market  Using a hierarchical Bayesian linear model, we have also simultaneously investigated the overall market characteristics and asset-specific tendencies regarding the effects of age and market capitalization on the power-law exponents 
We have found that the cryptocurrency market is highly heterogeneous regarding the trends exhibited by each cryptocurrency; however, only a small fraction of cryptocurrencies (10%) have power-law exponents neither correlated with age nor market capitalization  These associations have been mostly ignored by the current literature and are probably related to the still-early developmental stage of the cryptocurrency market as a whole 
Overall, 36% of cryptocurrencies present trends that do not systematically contribute to increasing or decreasing their power-law exponents as they age and grow in market capitalization  On the other hand, for 26% of cryptocurrencies, aging and growing market capitalization are both associated with a reduction in their power-law exponents, thus contributing to the rise in the frequency of large price variations in their dynamics 
Only about 28% of cryptocurrencies present trends in which the power-law exponents increase with age and market capitalization, favoring thus large price variations to become less likely  These results somehow juxtapose with findings about the increasing informational efficiency of the cryptocurrency market  
In fact, if on the one hand the cryptocurrency market is becoming more informationally efficient, then on the other our findings indicate that there is no clear trend toward decreasing the risks of sizable variations in the prices of most considered cryptoassets  In other words, risk and efficiency thus appear to be moving towards different directions in the cryptocurrency market 
To conclude, we hope that our findings will contribute significantly to the better understanding of the dynamics of large price variations in the cryptocurrency market as a whole, and not just for a small subset of selected digital assets, which is especially relevant due to the diminishing concentration of market capitalization among the top digital currencies, and also because of the considerable impact these new assets may have in our increasingly digital economy 
Our results are based on time series of the daily closing prices (in USD) for all cryptoassets listed on CoinMar-ketCap (coinmarketcap com) as of 25 July 2022 [see Supplementary Figure (a) for a visualization of the increasing number cryptoassets listed on CoinMarketCap since 2013]  These time series were automatically gathered using the cryptoCMD Python package and other information such as the tags associated with each cryptoasset were obtained via the CoinMarketCap API  
In addition, we have also obtained the daily market capitalization time series (in USD) from all cryptoassets which had this information available at the time  Earliest records available from CoinMarketCap date from 29 April 2013 and the latest records used in our analysis correspond to 25 July 2022  Out of 9943 cryptocurrencies, we have restricted our analysis to the 7111 with at least 200 price-return observations 
The median length of these time series is 446 observations [see the distribution of series length in Supplementary Figure   We have estimated the power-law behavior of the return distributions by applying the Clauset-Shalizi-Newman method to the return time series r t   In particular, we have sampled each of these time series using an expanding time window that starts at the hundredth observation and grows in weekly steps (seven data points each step) 
For each position of the expanding time window, we have separated the positive returns from the negative ones and applied the Clauset-Shalizi-Newman method to each set  This approach consists of obtaining the maximum likelihood estimate for the power-law exponent, α = 1 + n/ (∑ n t=1 ln r t /r min ) , where r min is the lower bound of the power-law regime and n is the number of (positive or negative) return observations in the power-law regime for a given position of the expanding time window 
The value r min is estimated from data by minimizing the Kolmogorov-Smirnov statistic between the empirical distribution and the power-law model  The Clauset-Shalizi-Newman method yields an unbiased and consistent estimator , in a sense that as the sample increases indefinitely, the estimated power-law exponent converges in distribution to the actual value 
Moreover, we have used the implementation available on the powerlaw Python package   In addition to obtaining the power-law exponents, we have also verified the adequacy of the power-law hypothesis using the procedure originally proposed by Clauset et al  as adapted by Preis et al    This procedure consists of generating synthetic samples under the power-law hypothesis with the same properties of the empirical data under analysis (that is, same length and parameters α and r min ), adjusting the simulated data with the power-law model via the Clauset-Shalizi-Newman method, and calculating the Kolmogorov-Smirnov statistic (κ syn ) between the distributions obtained from the simulated samples and the adjusted power-law model 
Next, the values of κ syn are compared to the Kolmogorov-Smirnov statistic calculated between empirical data and the power-law model (κ)  Finally, a p-value is defined by calculating the fraction of times for which κ syn > κ  We have used one thousand synthetic samples for each position of the expanding time window and the more conservative 90% confidence level (instead of the more lenient and commonly used 95% confidence level), such that the power-law hypothesis is rejected whenever p-value ≤ 0 1 
We have estimated the effects of age and market capitalization on the power-law exponents associated with positive or negative returns of a given cryptocurrency using the linear model where α t represents the power-law exponent, log c t is the logarithm of the market capitalization, and y t is the age (in years) of the cryptocurrency at t-th observation 
Moreover, K is the intercept of the association, while C and A are linear coefficients quantifying the effects of market capitalization and age, respectively  Finally, N (µ, σ ) stands for the normal distribution with mean µ and standard deviation σ , such that the parameter ε accounts for the unobserved determinants in the dynamics of the power-law exponents 
We have framed this problem using the hierarchical Bayesian approach such that each power-law exponent α t is nested within a cryptocurrency with model parameters considered as random variables normally distributed with parameters that are also random variables  Mathematically, for each cryptocurrency, we have
12/16 where µ K , σ K , µ C , σ C , µ A , and σ A are hyperparameters  These hyperparameters are assumed to be distributed according to distributions that quantify the overall impact of age and market capitalization on the cryptocurrency market as a whole  We have performed this Bayesian regression for exponents related to positive and negative returns separately, and used noninformative prior and hyperprior distributions in order not to bias the posterior estimation  
Specifically, we have considered and ε ∼ U (0, 10 2 ) , where U (a, b) stands for the uniform distribution in the interval [a, b] and Inv−Γ(θ , γ) represents the inverse gamma distribution with shape and scale parameters θ and γ, respectively  For the numerical implementation, we have relied on the PyMC Python package and sampled the posterior distributions via the gradient-based Hamiltonian Monte Carlo no-U-Turn-sampler method 
We have run four parallel chains with 2500 iterations each (1000 burn-in samples) to allow good mixing and estimated the Gelman-Rubin convergence statistic (R-hat) to ensure the convergence of the sampling approach (R-hat was always close to one)  In addition, we have also verified that models describing the power-law exponents as a function of only age (C → 0 in Eq  3) or only market capitalization (A → 0 in Eq  3) yield significantly worse descriptions of our data as quantified by the Widely Applicable Information Criterion (WAIC) and the Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (PSIS-LOO) (see Supplementary Table )  ) is larger than r 90 estimated from negative returns (r − 90 ) 
This fraction is calculated only for weeks in which the power-law hypothesis is not rejected for both tails  The percentage of cryptoassets for which r + 90 > r − 90 is shown in the panels  The first column of panels depicts the results when considering data from all cryptocurrencies, while the second and third columns present the results for the top 2000 and top 200 cryptocurrencies by market capitalization, respectively 
Sampling issues refer to missing data and problems caused by prices of cryptoassets decreasing to zero  We note that these distributions barely change when considering only cryptocurrencies without any sampling issue  Indeed, the distributions in this figure are not significantly distinguishable from their counterparts in Fig  (two-sample Kolmogorov-Smirnov test, p > 0 05) 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 35% of the associations are classified as mixed trends (green rectangles), 37% are increasing trends (blue rectangles), and 18% are decreasing trends (red rectangles) 
Context 2: 2015-05-14 Assigned to ROVI GUIDES, INC  reassignment ROVI GUIDES, INC  MERGER (SEE DOCUMENT FOR DETAILS)  Assignors: TV GUIDE, INC 
2015-05-14 Assigned to UV CORP  reassignment UV CORP  MERGER (SEE DOCUMENT FOR DETAILS)  Assignors: UNITED VIDEO PROPERTIES, INC 
2015-05-14 Assigned to TV GUIDE, INC  reassignment TV GUIDE, INC  MERGER (SEE DOCUMENT FOR DETAILS)  Assignors: UV CORP 
Methods and systems are described herein for quickly and easily displaying supplemental information about an event occurring in a media asset  In some embodiments, a media application may use a content-recognition module to determine the context of an event and distribute itemized tasks to multiple entities in order to generate the supplemental information about the event 
While viewing media assets (e g , a television program), users may wish to learn more information about an event (e g , a statement made by a person appearing in the media asset, the validity of a claim in an advertisement, etc ) occurring in the media asset  While some media assets allow a user to select additional options or added features (e g , pop-up biographies about the cast and crew), when the added features appear and what topic the added features concern are determined by the content producer and not the user  Furthermore, as the added feature is derived from the content producer, the added feature may be biased or may present limited viewpoints about an event  Therefore, added features provided by a content producer may not provide the added information about an event that a user desires 
In order to gain the added information that a user desires, the user may use additional devices (e g , a laptop computer) to search (e g , using an Internet search engine) for more information about the event  However, without knowing the proper context (e g , who said the statement, what was the tone of the statement, when was the statement said, etc ) of the event or what search terms to use to describe the context of the event (e g , how to describe the tone of the statement), a user may not be able to determine (even using a search engine) more information about the event  Moreover, the use of general search terms may not provide the accuracy or precision needed by the user  Furthermore, even if a user may eventually determine the information, the effort and time required may distract the user from the media asset 
Accordingly, methods and systems are described herein for quickly and easily displaying supplemental information about an event occurring in a media asset  In some embodiments, a media application may use a content-recognition module to determine the context of an event in a media asset and distribute itemized tasks to multiple users in order to generate the supplemental information about the event  The context-recognition module prevents the user from being distracted from the media asset (e g , while the user attempts to describe the context of the event or search for information about the event)  In addition, by distributing tasks to multiple entities (e g , crowd-sourcing), the media application may collect large amounts of information in relatively short periods of time (or in real-time) and aggregate and/or filter the information to generate the supplemental information about the event based on multiple viewpoints and/or sources  By using multiple viewpoints and/or sources, the media application enhances the completeness (e g , by providing unbiased information) and accuracy of the supplemental information 
For example, when a statement or action is made by a character or person appearing on a media asset (e g , a television program), a user may request supplemental information about the statement or action  In response, the media application may determine the context of the statement (e g , who said the statement and to what the statement was referring) or action (e g , what was the reason for the action)  After determining the context of the statement or action, the media application may itemize into tasks the additional information it requires in order to generate the supplemental information  The media application may then transmit requests including the tasks to a plurality of other users  Based on the responses from the plurality of other users, the media application may generate the supplemental information for display to the user 
In some embodiments, a media application may use multiple types of content-recognition modules and/or algorithms to determine the context of an event  For example, the media application may process data associated with the event in order to determine the context of an event  In some embodiments, processing the various types of data may include cross-referencing the data in a database indicating the different contexts the event may have 
In some embodiments, a media application may generate supplemental information about an event in a media asset in response to a user request  In order to generate the supplemental information, the media application may transmit, to multiple users, a request for additional information regarding a context of an event shown in a media asset  Upon receiving messages from the plurality of users that include the requested additional information, the media application may generate the supplemental information associated with the context of the event based on the messages 
It should be noted, the systems and/or methods described above may be applied to, or used in accordance with, other systems, methods and/or apparatuses 
FIG  9 is a flowchart of illustrative steps for generating supplemental information based on additional information provided by a plurality of users in accordance with some embodiments of the disclosure 
Accordingly, methods and systems are described herein for quickly and easily displaying supplemental information about an event occurring in a media asset  The methods and systems described herein alleviate the need for a user to determine the proper context (e g , who said a statement, what was the tone of the statement, when was the statement said, etc ) of an event in a media asset, or the search terms to use to describe the event (e g , the proper search terms to describe the tone of the statement), in order to determine more information about the event  In addition, the methods and systems increase the completeness and accuracy of the information compared to information gathered using traditional searching methods (e g , an Internet search engine), without distracting the user from the media asset 
In some embodiments, a media application may receive a user input from a user device for supplemental information about the context of an event shown in a media asset  The media application may determine additional information required to generate the supplemental information about the context of the event shown in a media asset, and transmit requests for the additional information to one or more users  The media application may receive one or more messages, which include the requested additional information, from the one or more users and generate the supplemental information based on the one or more message  The media application may then instruct the user device to display the supplemental information 
As used herein, “supplemental information” refers to any information related to or associated with an event in a media asset  For example, supplemental information may include, but is not limited to, the verification of a statement or claim in a media asset, further descriptions and/or information about objects or entities shown and/or described in a media asset, and/or any other information, including, but not limited to, a video or audio segment, that may interest a user about an event in a media asset  In some embodiments, the media application may generate supplemental information based on one or more pieces of additional information 
As used herein, “additional information” refers to any information used to generate supplemental information  For example, in an embodiment in which supplement information is the verification of a statement made by a person displayed in a media asset, and a request for the additional information from the media application includes a request for a fact needed to verify the factual basis of the statement, the additional information may be the fact used to verify the statement  For example, if an advertisement claims to have the best product on the market, the media application may use additional information such as the name of the product in question, a list of all other products in the market, and the results of a comparison study of the product in question to all other products to determine whether or not the product is actually the “best” product on the market  Additionally or alternatively, the media application may request industry and/or user reviews related to the event (e g , reviews indicating the quality of the product)  The media application may then use the information in the reviews to generate the supplemental information 
As used herein, an “event” is any action (e g , a verbal statement, opinion and/or physical movement), segment (e g , a portion of a news broadcast featuring a particular topic), or other occurrence during a media asset that may be of particular interest to a user  For example, in some embodiments an event may be a statement or gesture made by a character or person in a media asset affirming or denying a claim 
As referred to herein, the terms “media asset” and “content” should be understood to mean an electronically consumable user asset, such as television programming, as well as pay-per-view programs, on-demand programs (as in video-on-demand (VOD) systems), Internet content (e g , streaming content, downloadable content, Webcasts, etc ), video clips, audio, content information, pictures, rotating images, documents, playlists, websites, articles, books, electronic books, blogs, advertisements, chat sessions, social media, applications, games, and/or any other media or multimedia and/or combination of the same  Media applications also allow users to navigate among and locate content  As referred to herein, the term “multimedia” should be understood to mean content that utilizes at least two different content forms described above, for example, text, audio, images, video, or interactivity content forms  Content may be recorded, played, displayed or accessed by user equipment devices, but can also be part of a live performance 
As referred to herein, the phrase “user equipment device,” “user equipment,” “user device,” “electronic device,” “electronic equipment,” “media equipment device,” or “media device” should be understood to mean any device for accessing the content described above, such as a television, a Smart TV, a set-top box, an integrated receiver decoder (IRD) for handling satellite television, a digital storage device, a digital media receiver (DMR), a digital media adapter (DMA), a streaming media device, a DVD player, a DVD recorder, a connected DVD, a local media server, a BLU-RAY player, a BLU-RAY recorder, a personal computer (PC), a laptop computer, a tablet computer, a WebTV box, a personal computer television (PC/TV), a PC media server, a PC media center, a hand-held computer, a stationary telephone, a personal digital assistant (PDA), a mobile telephone, a portable video player, a portable music player, a portable gaming machine, a smart phone, or any other television equipment, computing equipment, or wireless device, and/or combination of the same 
In some embodiments, the user equipment device may have a front facing screen and a rear facing screen, multiple front screens, or multiple angled screens  In some embodiments, the user equipment device may have a front facing camera and/or a rear facing camera  On these user equipment devices, users may be able to navigate among and locate the same content available through a television  Consequently, media may be available on these devices, as well  The media provided may be for content available only through a television, for content available only through one or more of other types of user equipment devices, or for content available both through a television and one or more of the other types of user equipment devices  The media applications may be provided as on-line applications (i e , provided on a website), or as stand-alone applications or clients on user equipment devices  Various devices and platforms that may implement media applications are described in more detail below 
In some embodiments, a media application may transmit, to a plurality of users, a request for additional information regarding a context of an event shown in a media asset  As used herein, a “plurality of users” may include, but is not limited to any device, entity, or source of information that may process a request for additional information  For example, the plurality of users may include a person operating a user equipment device  In some embodiments, the person may receive (e g , via e-mail, Internet posting, advertisement, or any other applicable information delivery method) the request from the media application for additional information, and in response generate a message (e g , via a return e-mail, an answer to the Internet posting, a user input in the advertisement, or any other applicable method of transmitting information) that includes the additional information  It should be noted that in some embodiments, transmitting a request to a plurality of users may also include querying one or more databases (e g , an Internet search engine or any other storage device, including, but not limited to, databases containing previously generated supplemental information and/or additional information) or consulting one or more data gathering services (e g , a intelligent personal assistant application) for the additional information 
In some embodiments, a media application may use a content-recognition module or algorithm to determine the context of an event and distribute itemized tasks to multiple users in order to generate the supplemental information about the event  The content-recognition module may use object recognition techniques such as edge detection, pattern recognition, including, but not limited to, self-learning systems (e g , neural networks), optical character recognition, on-line character recognition (including but not limited to, dynamic character recognition, real-time character recognition, intelligent character recognition), and/or any other suitable technique or method to determine the objects and/or characteristics in media assets  For example, the media application may receive media assets in the form of a video  The video may include a series of frames  For each frame of the video, the media application may use a content-recognition module or algorithm to determine the context (e g , the person that is speaking or a facial gesture affirming or denying a statement) of an event occurring during the frame or series of frames 
In some embodiments, the content-recognition module or algorithm may also include speech recognition techniques, including but not limited to Hidden Markov Models, dynamic time warping, and/or neural networks (as described above) to translate spoken words into text  The content-recognition module may also use other techniques for processing audio and/or visual data  For example, the media application may monitor the volume of a statement in a media asset to determine the tone of the statement (e g , a high volume may indicate an angry tone) 
In addition, the media application may use multiple types of optical character recognition and/or fuzzy logic, for example, when determining the context of a keyword(s) retrieved from data (e g , media data, translated audio data, subtitle data, user-generated data, etc ) associated with the media asset (or when cross-referencing various types of data with databases indicating the different contexts of events as described below)  For example, the particular data field may be a textual data field  Using fuzzy logic, the system may determine two fields and/or values to be identical even though the substance of the data field or value (e g , two different spellings) is not identical  In some embodiments, the system may analyze particular data fields of a data structure or media asset frame for particular values or text  The data fields could be associated with characteristics, additional information, and/or any other data required for the function of the embodiments described herein  Furthermore, the data fields could contain values (e g , the data fields could be expressed in binary or any other suitable code or programming language) 
As used herein, the “context” of an event refers to the set of circumstances or facts that surround a particular event that influence or affect the meaning of the event  For example, when determining the context of a written and/or spoken statement, the media application may determine who or what authored/stated the statement, the written and/or spoken words and/or other statements that preceded and/or followed the statement, the tone of the statement, and/or any other conditions that may alter the connotation of the statement 
FIG  1 shows an illustrative example of a media application that may be used to display supplemental information in accordance with some embodiments of the disclosure  Display 100 illustrates a display on a user device displaying a media asset  Display 108 illustrates a display featuring supplemental information as described and/or generated in FIGS  6-9  It should be noted that display 100 and display 108 may be presented on any of the devices shown in FIGS  3-4  For example, in some embodiments, display 100 and display 108 may be displayed on user equipment 402, 404, and/or 406 (FIG  4) 
In FIG  1, display 100 represents a display of a media asset (e g , a streaming television program) on a user device (e g , user equipment 402, 404, and/or 406 (FIG  4))  Display 100 includes entity 102 and entity 104  In display 100, entity 104 is currently speaking as indicated by event 106  As shown in FIG  1, event 106 is a statement (e g , “We export a lot of coal”) by a person in the media asset 
In some embodiments, display 108 represents the continued display of the media asset on a user device, after a user has requested supplemental information about event 106  For example, a media application may have received a user input (e g , via user input interface 310 (FIG  3)) while entity 104 was speaking  Using the systems and methods described herein (e g , FIGS  6-9), the media application generated supplemental information 110  Supplemental information 110 represents more information about event 106 
For example, the media application (e g , media application 206 (FIG  2)) may have determined the context of event 106  Specifically, the media application may determine via a content-recognition module or algorithm the words spoken and/or actions by the person during the event  Additionally or alternatively, the media application may analyze the words and/or action during a predetermined amount of time (e g , ten seconds) before and/or after the event (e g , in order to better understand the context of the event)  Furthermore, by cross-referencing the words and/or other information obtained by the content-recognition module (e g , as discussed below in relation to FIG  5) with a database, the content-recognition module determines that the term “we,” the person in the media asset refers to an organization or body
Context 3: Xpp-pdf support utility
Xpp-pdf support utility
PATENT, TRADEMARK
& COPYRIGHT !
Reproduced with permission from BNA’s Patent,Trademark 11/20/09, 11/20/2009  Copyright ஽ 2009 by The Bu-reau of National Affairs, Inc  (800-372-1033) http://www bna com As the patent community anticipates a decision by the U S  Supreme Court on subject matter patentability, recent rulings by the Federal Circuit and the Board of Patent Appeals and Interferences suggest strategies for preparing method patent applications that will sur- vive the Federal Circuit’s ‘‘machine-or-transformation’’ test 
The Changing Landscape of Method Claims in the Wake of In re Bilski:What We Can Learn from Recent Decisions of Federal Courts and the Board ofPatent Appeals rulings on software-based and other business methodpatent applications 
On review before the high court is the en banc ruling ‘‘Pure’’ business methods are out  Algorithms by the U S  Court of Appeals for the Federal Circuit1 are out  Machines and data transformations that, in order to be eligible for patent protection, an in- ventive method must either be tied to a machine or re- While the patent community waits for the Supreme cite a transformation of an article 2 This ‘‘machine-or- Court’s decision in Bilski v  Kappos, No  08-964 (U S 
transformation’’ test replaced the Freeman-Walter- argued Nov  9, 2009) (79 PTCJ 33, 11/13/09), patent ap- Abele3 test and the ‘‘useful, concrete and tangible plicants seeking to write patentable claims are stuckwith trying to conform to the lower courts’ most recent 1 In re Bilski, 545 F 3d 943, 88 USPQ2d 1385 (Fed  Cir 
2008) (en banc) (77 PTCJ 4, 11/7/08) 
2 ‘‘The machine-or-transformation test is a two-branched Adriana Suringa Luedke and Bridget M  Hay- inquiry; an applicant may show that a process claim satisfies den are lawyers at Dorsey & Whitney, Min- § 101 either by showing that his claim is tied to a particular neapolis  Luedke can be reached at machine, or by showing that his claim transforms an article ’’ leudke adriana@dorsey com  Hayden can be reached at hayden bridget@dorsey com  3 In re Freeman, 573 F 2d 1237, 197 USPQ 464 (C C P A 
1978); In re Walter, 618 F 2d 758, 205 USPQ 397 (C C P A 
COPYRIGHT ஽ 2009 BY THE BUREAU OF NATIONAL AFFAIRS, INC 
result’’ inquiry advocated in State Street,4 each of gregating, and selling real estate property and claims which had been applied by the Federal Circuit and its reciting a method of performing tax-deferred real estate predecessor court in various cases, and both of which property exchanges were not statutory under Section 101  Since no machine was recited, the only issue be- In this article, we examine the 2008 decision of the fore the court was whether the claims met the ‘‘trans- Federal Circuit, federal district court decisions, and de- formation’’ prong of the Bilski test 13 The court held cisions of Patent and Trademark Office’s Board of that the claims ‘‘involve[d] only the transformation or Patent Appeals and Interferences  Based upon the out- manipulation of legal obligations and relationships’’ comes in these cases, we offer guidance as to what is that did not qualify under Bilski 14 patent-eligible under 35 U S C  § 101, strategies for pre- Concerning the recitation of the ‘‘creation of deed- senting methods in patent applications and claiming shares’’ in some of the claims, the court found that the these methods, and possible ‘‘fixes’’ for applications deedshares themselves were not physical objects, but drafted pre-Bilski that must now withstand scrutiny un- only represented intangible legal ownership interests in der the new machine-or-transformation test 
property 15 Therefore, the creation of deedshares wasnot sufficient to establish patent eligibility under Bil- A number of recent federal court and board decisions have applied the patent eligibility test set forth in Bilski, implemented step to an otherwise obvious method was not sufficient to avoid invalidity of the claim  In KingPharmeuticals Inc  v  Eon Labs Inc ,17 the district court held invalid claims to a method of increasing the oral Several cases have addressed (and rejected) claims bioavailability of metaxalone because the claims were obvious over the prior art asserted by the accused in- In In re Ferguson,6 the Federal Circuit reviewed the board’s rejection of claims directed to a method of mar- Two dependent claims added a step of informing the keting a product and a ‘‘paradigm’’ for marketing soft- patient of certain results, which the patentee argued ware as nonstatutory subject matter under Section was not obvious  The court rejected this argument, con- 101 7 The appellate court affirmed the board’s rejection, cluding that ‘‘[b]ecause the food effect is an inherent concluding that the method claims were neither tied to property of the prior art and, therefore, unpatentable, a particular machine or apparatus nor did they trans- then informing a patient of that inherent property is form a particular article into a different state or thing 8 The court defined a machine broadly as ‘‘a concrete The court also commented that the added step of in- thing, consisting of parts, or of certain devices or com- forming the patient did not meet the patent eligibility binations of devices,’’ which did not include the ‘‘shared standard set forth in Bilski because the step did not re- marketing force’’ to which the method claims were quire use of a machine or transform the metaxalone into a different state or thing 19 Notably, this conclusion The claims directed to a ‘‘paradigm’’ were non- runs counter to the Supreme Court’s instruction that statutory because the claims did not fall within any of claims are to be examined ‘‘as a whole’’ and not dis- the four statutory categories (machines, manufactures, sected into old and new elements and that are evaluated compositions of matter and processes)  Concerning the two closest possible categories, the court concluded Recent board decisions have been consistent with the that the claimed paradigm was not a process, because holdings of the federal courts  For example, in Ex parte no act or series of acts was required, and was not a Roberts,21 the board found ineligible under Section 101 manufacture, because it was not a tangible article re- a ‘‘method of creating a real estate investment instru- sulting from a process of manufacture 10 Concerning ment adapted for performing tax-deferred exchanges’’ the recitation of a ‘‘marketing company’’ in the para- because the claim did not satisfy either the machine or digm claims, the court concluded that the patent appli- cants did ‘‘no more than provide an abstract idea—a Similarly, in Ex parte Haworth,23 a method for ‘‘at- business model for an intangible marketing com- tempting to collect payments from customers having delinquent accounts concurrently with a partner that In Fort Properties Inc  v  American Master Lease owns the delinquent accounts’’ was found to be patent LLC,12 the California district court held that claims re- ineligible because the claim wording was ‘‘broad in that citing a series of transactions involving acquiring, ag- 1980); In re Abele, 684 F 2d 902, 214 USPQ 682 (C C P A 
4 State Street Bank & Trust Co  v  Signature Financial 16 See Ex parte Roberts , 2009-004444 at 4-5 (B P A I  June Group, 149 F 3d 1368, 1370, 47 USPQ2d 1596 (Fed  Cir  1998) 19, 2009) (holding a ‘‘method of creating a real estate invest- ment instrument adapted for performing tax-deferred ex- changes’’ patent ineligible as not passing the machine-or- 7 The court accepted the board’s definition of ‘‘paradigm’’ 17 593 F  Supp 2d 501 (E D N Y  2009) 
to mean ‘‘a pattern, example or model ’’ Id  at 1362 
20 See Diamond v  Diehr, 450 U S  175, 188 (1981) 
21 No  2009-004444 (B P A I  June 19, 2009) 
12 2009 WL 249205, *5 (C D  Cal  Jan  22, 2009) 
23 No  2009-000350 (B P A I  July 30, 2009) 
it refers generally to extending an offer, receiving an machine  Accordingly, the process claims       are not acceptance, and paying a commission’’ and did not in- voke, recite or limit the method of implementation us-ing any particular machine or apparatus 24 The court also evaluated similar claims that recited the use of a ‘‘comparator’’ to perform the recited pixel- B  Software Claims Not Expressly Tied to a ‘Particular by-pixel comparison and held that this recitation also did not mandate a machine 29 While the court acknowl-edged that software was offered as one ‘‘option,’’ the Other cases have addressed software methods where court concluded that the claimed function of the com- the claim language was either not expressly tied to com- parator could also be performed in one’s mind or on pa- puter hardware components or the ties to computer per such that a machine was not required  The court components were somewhat ambiguous  In several further noted that, even though the ‘‘comparator’’ was cases, courts have rejected the recitation of generic defined as a ‘‘device,’’ ‘‘the use of the term ‘device’ is computer components as sufficient to satisfy the ‘‘ma- not synonymous with machine ’’30 As a result, none of chine’’ prong of the Bilski test  A number of these deci- the claims at issue met the ‘‘machine’’ prong of the Bil- sions also addressed the ‘‘transformation’’ prong of the Concerning the ‘‘transformation’’ prong, the court re- In Research Corporation Technology Inc  v  Mi- lied in particular upon the Abele decision in expanding crosoft Corp ,25 the district court considered the patent the requirements of this test by requiring that the eligibility of method claims in six patents directed to claimed transformation process be both ‘‘(1) limited to methods of halftoning of gray scale images by using a transformation of specific data, and 2) limited to a vi- pixel-by-pixel comparison of the image against a blue sual depiction representing specific objects or sub- noise mask  Relying on the Federal Circuit’s Bilski stances ’’31 It then concluded that a number of the analysis as well as a decision of its predecessor court, patent claims did not meet the second prong of this ex- In re Abele,26 the judge concluded that a number of the panded test because the claims did not ‘‘require any vi- sual depiction or subsequent display’’ even though the transformation test set forth in Bilski 27 claimed method did transform specific image data 32 Concerning the ‘‘machine’’ prong, the district court The district court also found other claims patent- found that the pixel-by-pixel comparison recited in the eligible under Section 101 because these claims recited claims did not require the use of a machine, but could the use of the comparison data ‘‘to produce a halftoned ‘‘dictate[d] a transformation of specific data, and [were] be done on a sheet of paper using a pen  The com- further limited to a visual depiction which represents parison uses formulas and numbers to generate a bi- specific objects ’’33 Thus, the patent eligibility of the nary value to determine the placement of a dot at a claims turned on whether the claims recited the use of location  Formulas and numbers not tied to a particu- the transformed data to generate a display 
lar machine cannot be patented, under the machine In DealerTrack Inc  v  Huber,34 the district court prong, even with a field-of-use limitation because granted a summary judgment of invalidity under § 101 they represent fundamental principles, and to do so of patent claims directed to ‘‘a computer aided method’’ would preempt the entire field  The patent claims      
of managing a credit application reciting the following do not mandate the use of a machine to achieve their algorithmic and algebraic ends  Simply because adigital apparatus such as a computer, calculator, or [A] receiving credit application data from a remote the like could assist with this comparison does not render it patent eligible material  RCT’s argument [B] selectively forwarding the credit application data that a pixel by its nature is electronic and therefore to remote funding source terminal devices; necessitates a machine is a post solution argumentand the Court rejects it  The claim construction specifies that the comparison is of a value to a mask 29 The term ‘‘comparator’’ was construed by the court to be (or set of values) to determine whether the dot is a ‘‘device (or collection of operations, as in software) that com- turned on at a specific location  This process does pares an input number (called the operand) to a number pre- not require a particular machine  The Bilski test is stored in the comparator (called the threshold) and produces clear: the process claims must be tied to a particular as output a binary value (such as ‘‘0,’’ zero) if the input is alge-braically less than the threshold [the result of comparing anoperand against a fixed threshold and setting an operand less 24 Id  at 9-10  See also, e g , Ex parte Farnes, 2009-002770 than the threshold to one value and an operand greater than (B P A I  June 2, 2009) (rejecting a method claim for develop- or equal to the threshold to another value], and produces the ing a solution to a customer experience issue including steps opposite binary value (such as ‘‘1,’’ one) if the input is algebra- of: ‘‘identifying a target customer,’’ ‘‘defining a current cus- ically greater than or equal to the threshold ’’ Id  at *17 (em- tomer experience,’’ ‘‘summarizing values and benefits’’ to pro- vide to the customer, and ‘‘identifying metrics for measuring success’’); Ex parte Salinkas, 2009-002768 (B P A I  May 18, 31 Id  at *9  Notably, Bilski concluded that the Abele visual 2009) (finding patent ineligible a method of launching a depiction was ‘‘sufficient’’ to establish transformation (545 knowledge network involving ‘‘selecting an executive spon- F 3d at 963), while the Research Corporation court went fur- sor,’’ ‘‘forming a core team of experts,’’ and ‘‘providing pre- ther by making visual depiction ‘‘required’’ to establish trans- 25 2009 WL 2413623 (D  Ariz  July 28, 2009) (78 PTCJ 432, 26 684 F 2d 902, 214 USPQ 682 (C C P A  1982) 
34 2009 WL 2020761 (C D  Cal  July 7, 2009) (78 PTCJ 341, PATENT, TRADEMARK & COPYRIGHT JOURNAL [C] forwarding funding decision data from at least tation of ‘over the Internet’ suffices to tie a process one of the remote funding source terminal de- claim to a particular machine’’ and concluded that it vices to the remote application entry and display The internet continues to exist despite the addition [D] wherein the selectively forwarding the credit ap- or subtraction of any particular piece of hardware  It may be supposed that the internet itself, rather than [E] sending at least a portion of a credit application any underlying computer or set of computers, is the to more than one of said remote funding sources ‘‘machine’’ to which plaintiff refers  Yet the internet is an abstraction  If every computer user in the world [F] sending at least a portion of a credit application unplugged from the internet, the internet would to more than one of said remote funding sources cease to exist, although every molecule of every ma- sequentially until a finding [sic ] source returns a chine remained in place  One can touch a computer or a network cable, but one cannot touch ‘‘the inter- [G] sending       a credit application       after a prede- Additionally, the court found that the recitation of the [H] sending the credit application from a first remote internet in this case merely constituted ‘‘insignificant funding source to a second remote funding extra-solution activity’’ and therefore did not qualify as a ‘‘particular machine’’ under Bilski 41 ‘‘[T]ossing in In concluding that the claim did not satisfy the Bilski references to internet commerce’’ was not sufficient to machine-or-transformation test, the court held that the render ‘‘a mental process for collecting data and weigh- claimed central processor, remote application and dis- ing values’’ patent-eligible 42 Additionally, ‘‘limiting’’ play device, and remote funding source terminal device the claim to use over the Internet was not a meaningful could be ‘‘any device’’ and did not constitute a ‘‘’par- limitation, such that the claims ‘‘broadly preempt the ticular machine’ within the meaning of Bilski ’’35 The fundamental mental process of fraud detection using court relied upon several board decisions to support its associations between credit cards ’’43 premise that ‘‘claims reciting the use of general purpose processors or computers do not satisfy the test ’’36 claim,44 notwithstanding the Federal Circuit’s holding In Cybersource Corp  v  Retail Decisions Inc ,37 the in In re Beauregard,45 the district court concluded that district court held claims for ‘‘a method for verifying the ‘‘there is at present no legal doctrine creating a special validity of a credit card transaction over the Internet’’ ‘‘Beauregard claim’’ that would exempt the claim from and ‘‘a computer readable medium containing program the analysis of Bilski ’’ Moreover, ‘‘[s]imply appending instructions for detecting fraud in a credit card transac- ‘A computer readable media including program instruc- tion       over the Internet’’ invalid under § 101 based tions’ to an otherwise non-statutory process claim is in- upon the court’s interpretation of Bilski 
sufficient to make it statutory ’’46 Consequently, this Concerning the method claim, the court considered claim also failed the Bilski test 
both the ‘‘transformation’’ and ‘‘machine’’ prongs of the In at least one instance, the U S  International Trade Bilski test  In concluding that there was no transforma- Commission has interpreted the ‘‘machine’’ prong of tion, the court focused on the intangibility of the ma- Bilski less stringently than did the district courts in the nipulated data  According to the court, transformation cases discussed above  In In the Matter of Certain Video is limited to transformation of a physical article or sub- Game Machines and Related Three-Dimensional Point- stance  Accordingly, the method claim did not qualify ing Devices,47 the accused infringer filed a motion for because the data representing credit cards did not rep- summary judgment alleging that the asserted claims resent tangible articles but instead an intangible series impermissibly sought to patent a mathematical algo- of rights and obligations existing between the account rithm  According to the movant, the recitations of a ‘‘3D pointing device,’’ ‘‘handheld device,’’ or ‘‘free space Concerning whether the claimed method was tied to pointing device’’ were not sufficient to tie the claims to a particular machine, the court assessed whether ‘‘reci- a particular machine, but served ‘‘only to limit the field-of-use of the claimed mathematical algorithm and [did] not otherwise impart patentability on the claimed math- Id  at *3  The court relied upon the holdings in Ex parte Gutta, No  2008-3000 at 5-6 (B P A I  Jan  15, 2009) (stating In denying the motion for summary judgment, the ‘‘[t]he recitation in the preamble of ‘[a] computerized method ITC first noted that, ‘‘[w]hile the ultimate determination performed by a data processor’ adds nothing more than a gen- of whether the asserted claims are patentable under eral purpose computer that is associated with the steps of the § 101 is a question of law, the Federal Circuit has ac- process in an unspecified manner ’’); Ex parte Nawathe, No 
2007-3360, 2009 WL 327520, *4 (B P A I  Feb  9, 2009) (finding‘‘the computerized recitation purports to a general purpose processor [], as opposed to a particular computer specifically programmed for executing the steps of the claimed method ’’); and Ex parte Cornea-Hasegan, No  2008-4742 at 9-10 (B P A I 
Jan  13, 2009) (indicating the appellant does not dispute ‘‘the recitation of a processor does not limit the process steps to any 44 Claims having this format are called ‘‘Beauregard’’ specific machine or apparatus ’’)  The court also cited Cyber- claims and were found to not be barred by the traditional source Corp  v  Retail Decisions Inc , (discussed below), in sup- printed matter rule in In re Beauregard, 53 F 3d 1583, 1584, 35 port of its interpretation of the required ‘‘particular machine ’’ 37 620 F  Supp  2d 1068, 92 USPQ2d 1011 (N D  Cal  2009) 47 2009 WL 1070801 (U S I T C  2009) 
knowledged that ‘there may be cases in which the legal given a dataset of feature vectors associated with the question as to patentable subject matter may turn on subsidiary factual issues’ ’’ (citation omitted)  In con- for each binary partition under consideration, rank- struing the claims, the tribunal found that there was a ing features using two-category feature ranking; and genuine dispute as to whether the claimed ‘‘devices’’represented a ‘‘particular machine’’ under the Bilski while the predetermined number of features has not test and whether the claimed ‘‘two-dimensional rota- yet been selected: picking a binary partition p; tional transform’’ was merely a mathematical calcula- selecting a feature based on the ranking for binary tion or instead meant ‘‘changing the mathematical rep- resentation of a two-dimensional quantity from oneframe of reference to a differently-oriented frame of ref- adding the selected feature to an output list if not al- erence’’ as asserted by the patentee  Additionally, the ready present in the output list and removing the se- dispute over the meaning of the claimed ‘‘two- lected feature from further consideration for the bi- dimensional rotational transform’’ also raised a dis- puted issue as to whether this element recited a trans- Notably, while the independent claim failed the formation that would qualify under the ‘‘transforma- machine-or-transformation test, its dependent claim tion’’ prong of Bilski  Given these disputed issues, the was eligible because it recited, ‘‘further comprising us- ITC concluded that it was inappropriate to grant sum- ing the selected features in training a classifier for clas- mary judgment as to the patent eligibility of the claims 
sifying data into categories ’’ In view of the specifica- A similar conclusion was reached in Versata Soft- tion, the board indicated that the ‘‘classifier’’ was a par- ware Inc  v  Sun Microsystems Inc ,48 in which the dis- ticular machine ‘‘in that it performs a particular data trict court denied the defendant’s motion for summary classification function that is beyond mere general pur- judgment of invalidity under Section 101 based upon pose computing ’’53 The board also concluded that the the Bilski court’s refusal ‘‘to adopt a broad exclusion claim ‘‘transforms a particular article into a different over software or any other such category of subject state or thing, namely by transforming an untrained matter beyond the exclusion of claims drawn to funda- classifier into a trained classifier ’’54 In Ex parte Casati,55 the board reversed the examin- Less stringent ‘‘machine’’ prong analyses are also er’s Section 101 rejection of a method claim reciting: found at the board level  For example, in Ex parteSchrader,50 the board held patent-eligible under Bilski A method of analyzing data and making predictions, reading process execution data from logs for a busi- A method for obtaining feedback from consumers re- ceiving an advertisement from an ad provided by anad provider through an interactive channel, the collecting the process execution data and storing the process execution data in a memory defining a ware-house; creating a feedback panel including at least one feed-back response concerning said advertisement; and analyzing the process execution data; generatingprediction models in response to the analyzing; and providing said feedback panel to said consumers, using the prediction models to predict an occurrence said feedback panel being activated by a consumer to of an exception in the business process 
provide said feedback response concerning said ad-vertisement to said ad provider through said interac- In this case, giving consideration to the specification, which ‘‘unequivocally describes the data warehouse aspart of the overall system apparatus, and subsequent Here, the board found ‘‘interactive channel’’ to be descriptions describe the memory/warehouse device in part of an ‘‘overall patent eligible system of appara- terms of machine executable functions,’’ the board con- tuses’’ when viewed in the context of the specification, cluded that ‘‘one of ordinary skill in the art would un- which included ‘‘the Internet and World Wide Web, In- derstand that the claimed storing of process execution teractive Television, and self service devices, such as In- data in a memory defining a warehouse constitutes formation Kiosks and Automated Teller Machines ’’51 patent-eligible subject matter under § 101 because the In another recent decision, Ex parte Forman,52 the memory/warehouse element ties the claims to a particu- board found a ‘‘computer-implemented feature selec- tion method’’ including a ‘‘classifier’’ eligible under Other recent board decisions have reached the oppo- Section 101 because it satisfied both the machine and transformation prong  Here, the ‘‘classifier’’ was recitedin a dependent claim, in which its independent claim re-cited: 53 Id  at 13 
54 Id  See also Ex parte Busche, No  2008-004750 (B P A I 
A computer-implemented feature selection method May 28, 2009) (holding a process claim and a computer pro- for selecting a predetermined number of features for gram product claim, each reciting training a machine, ‘‘are di- a set of binary partitions over a set of categories rected to machines that have such structure as may be adaptedby training



ground_truth: Power-law functions.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: How does a media application determine the context of an event?

Read the following text and answer briefly.

Context 2:  The content-recognition module or algorithm may also determine that the term “export” refers to shipping goods out of a country  The content-recognition module or algorithm may also determine that the term “a lot” refers to a particular numerical amount  Finally, the content-recognition module or algorithm may also determine that the term “coal” refers to a mineral of fossilized carbon 
The content-recognition module or algorithm may also determine the relationships between words and/or other information obtained by the content-recognition module  For example, by processing the relationship between the words, the media application determines that event 106 is a statement regarding a particular amount of a particular substance shipped out of a particular country  Therefore, the media application determines that the request for supplemental information is likely a request to determine the validity of the statement  The media application then generates the supplemental information 
The media application may also have stored supplemental information generated by previous requests (e g , supplemental information generated in response to the same or different user viewing the media asset at an earlier date), and display the supplemental information again during the event (either in response to a user input requesting supplemental information or automatically without a user requesting supplemental information) 
FIG  2 shows an illustrative example of a system that may be used to generate supplemental information (e g , supplemental information 110 (FIG  1)) based on additional information provided by a plurality of users in accordance with some embodiments of the disclosure  For example, in some embodiments, system 200 may be used to generate supplemental information (e g , supplemental information 110 (FIG  1)) on a display (e g , display 108 (FIG  1)) of a user device (e g , user equipment 402, 404, and/or 406 (FIG  4))  It should be noted that in some embodiments, the devices shown in FIG  2 may correspond to one or more devices in FIGS  3-4 
FIG  2 shows system 200  In system 200, a user is currently accessing a media asset on display 202  In some embodiments, display 202 may correspond to display 100 (FIG  1))  During an event (e g , event 106 (FIG  1)) a user may have requested supplemental information about an event (e g , event 106 (FIG  1)) in display 202 using user device 204  Media application 206, which in some embodiments, may be implemented on user device 204 or at a remote location (e g , supplemental information source 418 (FIG  4)), receives the request for supplemental information 
Media application 206 determines the context of the event (e g , who said the statement making up the event and to what the statement was referring)  After determining the context of the statement, the media application may itemize into one or more tasks, additional information (e g , facts) it requires in order to generate the supplemental information (e g , a verification or correction of the factual basis of the statement)  For example, if the event is a statement about the amount of coal that is exported from the United States (e g , as described in relation to FIG  1 above), media application 206 may determine the fact required to generate the supplemental information is the exact numerical amount of coal that is exported from the United States  The media application may then transmit requests for the additional information (e g , a request for the exact numerical amount of coal that is exported from the United States) to a plurality of other users 
In FIG  2, users operating user device 208, user device 210, and user device 212 represent a plurality of users  Having determined the additional information it requires in order to generate the supplemental information, media application 206 requests the additional information from the plurality of users  In system 200, media application 206 has transmitted the same task (e g , the same question) to each of the plurality of users  In some embodiments, one or more of the users may receive different tasks  For example, by breaking the additional information into small, independent tasks, media application 206 may increase the speed (e g , multiple users may work concurrently to solve different parts of a problem) and accuracy (e g , reducing the tasks to smaller, less complex problems reduces the chance of human error) of the additional information returned by the plurality of users 
In addition, by breaking the additional information into small, independent tasks, the plurality of users may not know to what they are contributing (enhancing the privacy of the user that requested the supplemental information), however, the plurality of users can still be effective in their individual tasks  In addition, by breaking the additional information into small, independent tasks, the media application may more easily outsource the requests for additional information  For example, one or more of the tasks used to generate the additional information may be the same as one or more of the tasks used to generate other additional information (e g , additional information used to generate different supplemental information in response to a request for supplemental information about the same or a different event issued by the same or a different user)  The response to each of the request and/or the additional information may be stored (e g , on any of the devices accessible by communications network 414 (FIG  4)) for subsequent retrieval 
Based on the responses, transmitted as messages including the additional information, from the plurality of other users, media application 206 may generate the supplemental information (e g , supplemental information 110 (FIG  1)) for display to the user on the user device 204  For example, media application may aggregate, append, and/or compare the additional information in each of the messages received from the plurality of users  The supplemental information may then be generated based on the aggregated, appended, and/or compared additional information (e g , as described in FIG  9 below) 
In some embodiments, the plurality of users may receive summary information about the event with the request for additional information  (e g , a video clip of a portion or segment of the media asset, a textual description, etc ), which may help the plurality of users provide additional information  For example, in some embodiments, the media application may instead of (or in addition to) determining the context of an event, determine a particular portion of the event that would be needed for the plurality of users to provide additional information about the event 
For example, the media application may use progress information associated with the progress of the media asset (e g , line 506 (FIG  5)) to determine at what point during the progression of the media asset the event occurred, and in response, transmit a portion of the media asset beginning ten second before that point and ending ten seconds after that point  For example, if the event is a statement made by a character or person in a media asset, the media application may determine when the statement began (e g , the point of progress of the media asset in which the statement began) and ended  The media application may then include the portion containing the entire statement (and the event) in the request for additional information sent to the plurality of users 
The selected portion may include any amount of summary information that the media application determines is necessary for the user or any one of the plurality of users to understand the main action sequence  This summary information (e g , a portion of the media asset) may be included with the request for additional information (e g , in a file transmitted with the request), or may be included with the generated supplemental information as a reference for the user  For example, the media application may select a segment of the play length of the media asset or a particular scene of the media asset, which includes the event, for to display to the plurality of users along with the request for additional information 
For example, if an event (e g , a statement) was in response to a question, the media application may also determine when the question began and ended, and send the entire question (or the play length of the media asset corresponding to the question) to the plurality of users as well  After determining the portion to provide to the plurality of users (e g , a segment including the ten seconds before and the ten seconds after the event), the media application may provide the summary information of the event and any other material needed by the plurality of users to understand the event and/or request for supplemental information from the user 
In some embodiments, a portion of the media asset containing the event, as selected by the media application, may also include any amount of the play length of the media asset, or any amount of scenes or segments from the media asset  In some embodiments, the portion may include segments of the play length of the media asset or scenes from the media asset that are not adjacent during the normal playback of the media asset  For example, in some embodiments, a portion of the media asset may include one or more sequences or scenes of interest to the plurality of users, even though the particular sequences or scenes are featured at different points in the play length of the media asset  The media application may determine the segments or scenes to include based on a content recognition file (e g , data structure 500 (FIG  5)) describing the media asset  For example, if a plot point or other information, which may be relevant to an event is displayed earlier in the media asset, the summary information may include a portion of the media asset displaying the plot point 
In some embodiments, the length of a portion may be determined based on the genre of the media asset  In some embodiments, the length of the portion may depend on a user profile for the user or for anyone of the plurality of users  For example, a user profile and/or a content recognition file (e g , data structure 500 (FIG  5)) may indicate that a particular user may require more or less additional content  For example, the user may be aware of particular characters or plot points in the media asset and, therefore, may not require the additional content to introduce those aspects 
In some embodiments, the plurality of users may receive a particular user interface, which organizes the data about the event (e g , a clip of the actual event, summary information about the event, information about the request for supplemental information issued by the user, etc )  The interface may also include an automatic submission form, which may be used to generate a message, which is sent to the media application 
In some embodiments, the media application may also receive user input from the user requesting the supplemental information that further affects the generation of supplemental information by the media application  For example, the user may request the supplemental information includes particular information (e g , the factual basis of a statement), may request a multimedia format of the supplemental information (e g , textual description, a video clip, etc ), may request a form of the supplemental information (e g , a short description about the event, an Internet link to other sources of information on the event, or a true or false designation about the event) by entering user inputs (e g , via user input interface 310 (FIG  3)) 
It should be noted that any information or process referred to in this disclosure that is referred to as being in response to a user input may alternatively and/or additionally be performed automatically by the media application (e g , via control circuitry 304 (FIG  3))  For example, in some embodiments, a user may request a true or false designation (e g , an on-screen pop-up box indicating whether an event was true or false)  Additionally and/or alternatively, in some embodiments, the true or false designation may appear automatically based on predetermined settings indicating to the media application to display a true or false designation in response to detecting an event 
In some embodiments, an indicator that supplemental information has previously been generated or is currently ready to generate (e g , a plurality of users are available) may be displayed to a user (e g , on display 100 (FIG  1) during the event)  The indicator may also indicate the particular information, the multimedia format, and/or the form of supplemental information that is available  An indicator may also appear with the supplemental information (e g , supplemental information 110 (FIG  1)), which allows the user to request additional supplemental information or provide feedback/responses (e g , rating the quality of the supplemental information) to the media application and/or plurality of users 
In some embodiments, a user may also access (e g , via selection of an indicator and/or automatically upon the supplemental information being generated) summary information about the event  For example, in some embodiments (e g , when the supplemental information is not generated in real-time), the media asset may have progressed to a different point by the time the supplemental information is ready for display  Therefore, the media application may need to provide a video clip of the event or other summary information, so that the user remembers about what or why the supplemental information was requested 
FIG  3 is a block diagram of an illustrative user equipment device in accordance with some embodiments of the disclosure  It should be noted that the components shown in FIG  3 may be used to store, receive, transmit, and/or display the media assets, additional information, and/or supplemental information as described herein  For example, media application 206 (FIG  2) may be implemented on user equipment device 300, and may issue instructions (e g , displaying supplemental information 110 (FIG  1)) via control circuitry 304 
Users may access media assets and the media application (and its display screens described above and below) from one or more of their user equipment devices  FIG  3 shows a generalized embodiment of illustrative user equipment device 300  More specific implementations of user equipment devices are discussed below in connection with FIG  4  User equipment device 300 may receive content and data via input/output (hereinafter “I/O”) path 302  I/O path 302 may provide content (e g , broadcast programming, on-demand programming, Internet content, content available over a local area network (LAN) or wide area network (WAN), and/or other content) and data to control circuitry 304, which includes processing circuitry 306 and storage 308  Control circuitry 304 may be used to send and receive commands, requests, and other suitable data using I/O path 302  I/O path 302 may connect control circuitry 304 (and specifically processing circuitry 306) to one or more communications paths (described below)  I/O functions may be provided by one or more of these communications paths, but are shown as a single path in FIG  3 to avoid overcomplicating the drawing 
Control circuitry 304 may be based on any suitable processing circuitry such as processing circuitry 306  As referred to herein, processing circuitry should be understood to mean circuitry based on one or more microprocessors, microcontrollers, digital signal processors, programmable logic devices, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), etc , and may include a multi-core processor (e g , dual-core, quad-core, hexa-core, or any suitable number of cores) or supercomputer  



ground_truth: It uses a content-recognition module or algorithm.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What did the court in In re Ferguson conclude about the transformation prong of the Bilski test?

Read the following text and answer briefly.

Context 3: ’’) 55 No  2009-005786 (B P A I  July 31, 2009) 
48 2009 WL 1084412, *1 (E D  Tex  March 31, 2009) 
56 Id  at 7  See also Ex parte Dickerson, No  2009-001172 at 49 Citing Bilski, 545 F 3d at 959 n  23 
16 (B P A I  July 9, 2009) (holding claims that ‘‘recite a comput- 50 No  2009-009098 (B P A I  Aug  31, 2009) 
erized method which includes a step of outputting information from a computer       are tied to a particular machine or appa- 52 No  2008-005348 (B P A I  Aug  17, 2009) 
PATENT, TRADEMARK & COPYRIGHT JOURNAL implemented methods ineligible under the Bilski test transformation test applied to this type of claim 63 because the claims failed to tie the method steps to any Then, applying the Bilski test, the board concluded that concrete parts, devices, or combinations of devices  For the claim did not qualify  According to the board, the example, in Ex parte Holtz,57 the board found ineligible under Section 101 a ‘‘method for comparing file tree de-scriptions’’ because the claim ‘‘obtains data (a file struc- does not transform physical subject matter and is not ture), compares data (file structures), generates a tied to a particular machine        Limiting the claims change log, and optimizes the change log without tying to computer readable media does not add any practi- these steps to any concrete parts, devices, or combina- cal limitation to the scope of the claim  Such a field- tions of devices’’ and the ‘‘file structures’’ did not repre- of-use limitation is insufficient to render an other- Similarly, in Ex parte Gutta,58 the board held ineli- gible under § 101 a ‘‘method for identifying one or moremean items for a plurality of items       having a sym- II  The Current Scope of Patent Eligibility bolic value of a symbolic attribute,’’ concluding that the These recent cases establish that some types of meth- claim ‘‘computes a variance and selects a mean item ods are clearly patent-eligible under Section 101, others without tying these steps to any concrete parts, devices, clearly are not eligible, and yet others may be depend- or combinations of devices’’ and ‘‘symbolic values are ing on how they are described and claimed 
neither physical objects nor do they represent physicalobjects ’’ First, the eligibility of system and apparatus claims is largely unaffected by the Bilski decision, with the ca- In contrast to the district court’s decision in Cyber- veat that such claims may be more closely scrutinized source Corp , discussed supra, in a recent board deci- for compliance with Diamond v  Diehr and Gottschalk sion, Ex parte Bodin,59 ‘‘a computer program product’’ v  Benson, which prohibit patenting of a claim directed was found to be patent-eligible subject matter as being to ‘‘laws of nature, natural phenomena, [or] abstract embodied in a ‘‘computer readable medium ’’ Here, the board considered whether the phrase ‘‘recorded on the Also, methods that are performed at least in part by a recording medium’’ as it is recited in the body of the machine qualify for patent eligibility under Section 101 
claims was the same as ‘‘recorded on a computer- Thus, for example, some computer-implemented and readable medium ’’ Acknowledging the differences be- software-related inventions remain patentable as long tween a statutory claim to a data structure stored on a as they are properly described and claimed as being computer readable medium compared to a nonstatutory performed by a computer or computer components 
claim to a data structure that referred to ideas reflected The tie to a machine, however, cannot merely be im- in nonstatutory processes, the board stated: ‘‘[w]hen plicit based upon the description and context of the ap- functional descriptive material is recorded on some plication or general language in the preamble of the computer-readable medium, it becomes structurally claim  Instead, the use of a machine to perform one or and functionally interrelated to the medium and will be more of the claimed functions must be expressly de- statutory in most cases since use of technology permits scribed in the body of the claim so as to be a meaning- the function of the descriptive material to be real- ful limitation on the claim  If a method claim can be read in such a way that all functions can be performed Similarly, in Ex parte Azuma,61 a claim to a ‘‘com- by a human, it will likely not pass the machine prong of puter program product       comprising: a computer us- able medium’’ was found to be directed to statutory The ‘‘Interim Examination Instructions for Evaluat- subject matter under § 101 because the language ‘‘com- ing Subject Matter Eligibility Under 35 U S C  § 101’’ re- puter usable medium’’ referred to tangible storage me- cently issued by the Patent and Trademark Office con- dia, such as a server, floppy drive, main memory and firm that the recitation of a general purpose computer hard disk as disclosed by appellant’s specification, and is sufficient to satisfy Section 101 where the general did not ‘‘implicate the use of a carrier wave ’’ purpose computer is ‘‘programmed to perform the pro- In an older decision, Ex parte Cornea-Hasegan,62 cess steps,       in effect, becom[ing] a special purpose however, the Board seemingly came to the opposite conclusion, holding that a claim reciting ‘‘a computer Concerning data transformation, there seems to be readable media including program instructions which agreement of the Federal Circuit and at least one dis- when executed by a processor cause the processor to trict court that a method that is both limited to transfor- perform’’ a series of steps was not patent-eligible under mation of specific data and limited to a visual depiction Bilski  The board first determined that ‘‘analysis of a representing specific objects or substances qualifies un- ‘manufacture’ claim and a ‘process’ claim is the sameunder 63 Id  at 11 
57 No  2008-004440 at 12-13 (B P A I  Aug  24, 2009) 
65 Diamond v  Diehr, 450 U S  175, 185, 205 USPQ 488 58 No  2008-004366 at 10-11 (B P A I  Aug  10, 2009) 
(1980); Gottschalk v  Benson, 409 U S  63, 67, 175 USPQ 673 59 No  2009-002913 (B P A I  Aug  5, 2009) 
60 Id  at 10 (comparing In re Lowry, 32 F 3d 1579, 1583-84, 66 ‘‘Interim Examination Instructions for Evaluating Sub- 32 USPQ2d 1031 (Fed  Cir  1994) to In re Warmerdam, 33 F 3d ject Matter Eligibility Under 35 U S C  § 101,’’ U S  Patent and 1354, 1361-62, 31 USPQ2d 1754 (Fed  Cir  1994)) 
Trademark Office, Aug  24, 2009, at 6 (78 PTCJ 530, 8/28/09) 
61 No  2009-003902 at 10 (B P A I  Sept  14, 2009) 
The authors’ recent experiences with examiners suggest that 62 No  2008-004742 (B P A I  Jan  13, 2009) 
the examiners are following these instructions 
der Section 101 67 Thus, claims analogous to those in In Concerning claims directed to computer program re Abele68 in which ‘‘data clearly represented physical products, one district court has held that appending ‘‘A and tangible objects, namely the structure of bones, or- computer readable media including program instruc- gans, and other body tissues [so as to recite] the trans- tions’’ to an otherwise non-statutory process claim is in- formation of that raw data into a particular visual depic- sufficient to make it statutory 72 The board has also tion of a physical object on a display’’ are patent- held ineligible claims to ‘‘a computer readable me- dia ’’73 The board has, however, also upheld the eligibil-ity of ‘‘a computer program product’’ as being embod- ied in a computer readable medium 74 Given these in- Bilski has had a significant impact in eliminating consistent decisions, the patent eligibility of claims in patent protection for inventions that are performed en- tirely by humans or can be interpreted as such if read Concerning claims directed to generalized computer broadly  This includes claims that describe processes processing functions, several Board decisions suggest for creating or manipulating legal and financial docu- that, absent a tie to a concrete real-world application, ments and relationships  In this area in particular, many such claims are likely to be deemed an ‘‘algorithm’’ un- pending applications filed prior to Bilski are no longer der Benson and therefore held to be non-statutory  75 patent-eligible, and many issued patents are no longer Any recitation of a specific field of use for the claimed valid  This retroactive impact of the Bilski decision is process or use of the outcome of such processes are troubling, given the investment in these patents and ap- also more likely to be found ‘‘field-of-use’’ or ‘‘post- plications, which have now been rendered essentially solution activity’’ limitations insufficient to render the worthless despite the suggestion in the Federal Circuit’s claim patent-eligible  Thus, the more tied a claimed pro- earlier State Street decision, now overruled, that such cess is to tangible results or particular applications (not claims qualified for patent protection 
just fields of use), the more likely it is to qualify under Inventions that do not fit within the four statutory categories are also not patent-eligible  The Federal Cir-cuit and the board have rejected claims directed to ‘‘a III  Presenting and Claiming Methods in Patent signal,’’ ‘‘a paradigm,’’ ‘‘a user interface’’ and ‘‘a corr-elator’’ on the basis that these items did not qualify as a ‘‘machine, manufacture, composition of matter or pro- Several strategies for describing and claiming meth- cess’’ under § 101  70 There is also an increasing focus ods or processes in patent applications may avoid or on the tangibility of the claimed invention in that, to minimize potential Section 101 problems 
qualify as a ‘‘machine’’ or ‘‘manufacture’’ under Section First, the description provided in a patent application should include well-defined steps or functions associ-ated with method or process  For example, when the claims include ‘‘initiating’’ method steps, a description Remaining areas of uncertainty concerning the scope of well-defined physical steps or functions for initiating of Section 101 include (1) what qualifies under Bilski as should be provided, and a concrete item, machine, de- a ‘‘transformation of an article or data,’’ (2) whether vice, or component that is responsible for the initiating claims to computer programs (Beauregard claims) function should be identified  For claiming ‘‘identify- qualify, and (3) whether internal computer processing ing’’ method steps, provide specific parameters for functionality not tied to a specific application or tan- making the identification, such as according to a speci- fied measurement 76 Where data is involved, the source Concerning data transformation, other than Abele- and type of data should be specified 
style claims discussed above, what qualifies as a data or Also, drawings should be provided that depict the article transformation remains unclear  Claims that concrete item, device, component or combination have been held not to meet the transformation prong in- thereof, and each method or process step or function clude claims directed to the creation or manipulation of should be linked expressly to at least one item, device data representing an intangible series of rights and ob- or component in the drawings that performs the step or ligations (e g , credit card data) and claims directed to function  Broadening language indicating that other the transformation or manipulation of legal obligations components may also be used to perform the function and relationships  Beyond these specific examples, it is may also be included to avoid an unduly narrow inter- difficult to predict what will or will not qualify as a data or article transformation under Bilski 
The claims should affirmatively claim the device, ma- chine or component performing each step or function 
67 In re Bilski, 545 F 3d at 963; Research Corporation Tech- For computer or software-related inventions, the de- nologies, 2009 WL 2413623 at *9 
scription should specify that the software functionality 68 The claimed process involved graphically displaying vari- ances of data from average values wherein the data was X-rayattenuation data produced in a two dimensional field by a com- 72 Cybersource Corp , 620 F  Supp  2d at 1080 
puted tomography scanner  See In re Bilski, 545 F 3d at 962- 73 Cornea-Hasegan, No  2008-004742 
74 Ex parte Bodin, No  2009-002913 (B P A I  Aug  5, 2009) 
69 In re Bilski, 545 F 3d at 963 
75 E g , Ex parte Greene, No  2008-004073 (B P A I  Apr  24, 70 In re Nuijten 500 F 3d 1346, 1357, 84 USPQ2d 1495 (Fed 
2009); Daughtrey, No  2008-000202; Ex parte Arning, No 
Cir  2007) (74 PTCJ 631, 9/28/07) (signal); In re Ferguson, 558 2008-003008 (B P A I  Mar  30, 2009); Cybersource Corp , 620 F 3d 1359, 1366, 90 USPQ2d 1035 (Fed  Cir  2009) (77 PTCJ F  Supp 2d at 1080 (concerning claim 2) 
489, 3/13/09) (paradigm); Ex parte Daughtrey, No  2008- 76 See Brief of American Bar Association as Amicus Curiae 000202 (B P A I  Apr  8, 2009) (user interface); Ex parte Laba- Supporting Respondent, Bilski v  Kappos, No  08-964, ABA die, No  2008-004310 (B P A I  May 6, 2009) (correlator) 
Amicus Br  at 12-13 (U S  amicus brief filed Oct  2, 2009) (78 71 E g , Nuijten, 500 F 3d at 1356-7 
PATENT, TRADEMARK & COPYRIGHT JOURNAL is performed by a computer or computer components 
patent or published application, the option of importing Specificity as to the type of computer component per- subject matter into the specification is limited to ‘‘non- forming each function may be helpful in establishing essential’’ subject matter  In other words, the specifica- eligibility under the Bilski test 
tion can only be amended to disclose a machine for per-forming process steps as long as one skilled in the art IV  Fixing Pre-Bilski Applications to Meet the New would recognize from the original disclosure that the process is implemented by a machine  The key in mak- For patent applications filed prior to the Bilski deci- ing this type of amendment is avoiding (or overcoming) sion, it can be challenging to meet the new require- a rejection under 35 U S C  § 112, para  1, for lack of ments for patent eligibility, particularly when no ma- chine or transformations were expressly described in If incorporation by reference is not an option, a patent applicant may submit evidence, such as a decla- In some cases, there may be sufficient explicit de- ration by the inventor or a duly qualified technical ex- scription of a machine, e g , a computer, such that the pert, demonstrating that one skilled in the art would un- machine can be added into the body of the claims  For derstand the disclosed method to be one performed by example, patent applications for computer-related in- a machine  Unlike attorney argument, which can be dis- ventions sometimes contain a generic description of regarded, such evidence must be considered by the ex- computers that are used to perform the claimed method, and such a generic description may be suffi- One other option is to reformat the claims  Since Bil- cient to impart patent eligibility to the claims when the ski ostensibly does not apply to system and apparatus general-purpose computer is programmed to become a claims, in some instances it may be possible for an ap- plicant to convert his method claims into system claims For patent applications lacking in an explicit descrip- to avoid application of the Bilski test  This strategy, tion of any machine, however, the application may in- however, is unlikely to succeed where the patent speci- corporate by reference patents or publications that can fication does not describe such a system for implement- be used to bolster the specification and provide support ing the method and therefore does not provide the req- for the requisite claim amendments  When an applica- uisite disclosure of the claimed invention under Section tion incorporates by reference a U S  patent or pub- lished U S  patent application, any description from the incorporated references, whether or not the subject The future of the Bilski machine-or-transformation matter is ‘‘essential’’ to support the claims, may be im- test now rests with the Supreme Court  Regardless of ported into the specification  This option may enable the outcome of the appeal, however, it is clear that the importation of the requisite description of a machine, scope of statutory subject matter under Section 101 has which can then also be recited in the claims 77 When been narrowed  The Supreme Court now has a chance the document incorporated by reference is not a U S 
to clarify what has been excluded; it may even reject ormodify the Bilski machine-or-transformation test  How 77 Manual of Patent Examining Procedure, Eighth Ed , Rev 
this will affect the development and protection of cur- 7/2008, at § 608 01(P); see also 37 C F R  § 1 57 
rent and future technologies remains to be seen 
Source: http://www dorsey com/files/upload/luedke_bna_patent_journal_nov09 pdf
(resolução 404 2012 retificação 19062012)
RESOLUÇÃO Nº 404 , DE 12 DE JUNHO DE 2012 Dispõe sobre padronização dos procedimentos administrativos na lavratura de Auto de Infração, na expedição de notificação de autuação e de notificação de penalidade de multa e de advertência, por infração de responsabilidade de proprietário e de condutor de veículo e da identificação de condutor infrator, e dá outras providências 
Cheloidi e cicatrici ipertrofiche in dermatologia
a cura del dr  Antonio Del Sorbo - Specialista in Dermatologia e Venereologia antoniodelsorbo@libero it I Cheloidi di Alibert A volte una ferita anche apparentemente banale, guarisce lasciando una cicatrice voluminosa, rossastra e soprattutto antiestetica  I cheloidi sono cicatrici abnormi che possono far seguito a intervento chirurgico (es: tiroide, mammella, etc) e questo u



ground_truth: It required the transformation to be limited to specific data and a visual depiction representing specific objects or substances.