U.S. Patent and Trademark Office 

OFFICE OF THE CHIEF ECONOMIST 

IP DATA HIGHLIGHTS NO. 3 

Supplementary Material 

Adjusting to Alice: USPTO patent examination 

outcomes after Alice Corp. v. CLS Bank International 

Andrew Toole, PhD, Chief Economist 

Nicholas Pairolero, PhD, Economist 

April 2020 

The views expressed are those of the individual authors and do not necessarily reflect official positions of 

the Office of the Chief Economist or the U. S. Patent and Trademark Office. USPTO Economic Working 

Papers are preliminary research being shared in a timely manner with the public in order to stimulate 

discussion, scholarly debate, and critical comment. For more information about the USPTO’s Office of the 

Chief Economist, visit www.uspto.gov/economics 
 

 

 

 

 

Table of Contents 

Introduction......................................................................................................................................................................2 

Data construction and description.........................................................................................................................2 

Datasets on patent applications: public and proprietary versions..........................................................2 

Description of patent application characteristics...............................................................................................3 

Datasets at the technology class level..................................................................................................................4 

Restrict to applications filed before Alice, Berkheimer memorandum and USPTO 2019 PEG..................4 

Restriction to adjust for the Supreme Court Case Bilski v. Kappos.................................................................5 

Definition of “Alice-affected” and other technology groups.....................................................................5 

Outcome variable definitions.................................................................................................................................6 

Percentage of first office action Section 101 rejections....................................................................................6 

Section 101 first action examination uncertainty...............................................................................................6 

Methodology ...................................................................................................................................................................6 

Alice effect on the probability of first office action Section 101 rejections..........................................6 

Additional co-variates for non-random assignment.........................................................................................7 

Differential Alice impact due to abstract language............................................................................................7 

Assessment of pre-trends .......................................................................................................................................8 

Berkheimer memo and 2019 PEG effects on the probability of first office action Section 101 

rejections .......................................................................................................................................................................8 

Alice effect on first action examination uncertainty......................................................................................8 

Additional co-variates for non-random assignment.........................................................................................9 

Differential Alice impact due to abstract language............................................................................................9 

Assessment of pre-trends .......................................................................................................................................9 

Berkheimer memo and 2019 PEG effects on first action examination uncertainty........................10 

Findings............................................................................................................................................................................10 

Descriptive analysis.................................................................................................................................................10 

Regression results....................................................................................................................................................11 

Alice effect on first office action Section 101 rejections.................................................................................11 

Berkheimer memo and 2019 PEG effects on first office action Section 101 rejections............................14 

Alice effect on first action examination uncertainty ........................................................................................15 

Berkheimer memo and 2019 PEG effects on first action examination uncertainty...................................18 

Robustness checks for the impact of Alice......................................................................................................20 

Assessing dataset restrictions.............................................................................................................................20 

Assessing the Influence of Supreme Court Case Bilski v. Kappos.........................................................23 

Potential effects of Alice on first office action 102/103/112 rejections..............................................23 

U.S. patent classifications used in the report..................................................................................................27 

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Introduction 

This supplement describes the data and research methods used in “Adjusting to Alice: USPTO 

patent examination outcomes after Alice Corp. v. CLS Bank International” published in April 

2020. 

Data construction and description 

Database construction started with information on USPTO patent applications. That information 

was augmented in several ways to complete the datasets used in the empirical analysis. To 

evaluate our first examination outcome of interest – the likelihood of receiving a first office 

action with a rejection for patent-ineligible subject matter – we estimated models using patent 

application-level datasets. For our second examination outcome – uncertainty in patent 

examination – we aggregated the application-level datasets to the technology class level. 

Datasets on patent applications: public and proprietary versions 

Two patent application-level datasets were constructed for the empirical analysis. A public data 

version, which was used to estimate models evaluating the U.S. Supreme Court decision in Alice 

Corp. v. CLS Bank International (Alice), and a proprietary data version, which was used to 

evaluate the USPTO’s Berkheimer Memorandum and the January 2019 Revised Patent Subject 

Matter Eligibility Guidance (2019 PEG). The proprietary dataset was required for the examiner 

guidance analysis to avoid problems with missing public data. Patent application information is 

not publicly available until about 18 months after initial filing and the time dimension for the 

analysis extends through the end of January 2020. 

The public application datasets started with the Patent Examination Research Dataset (PatEx) 

described in Graham et al. 2018 (PatEx data are available at https://www.uspto.gov/learningand-resources/electronic-data-products/patent-examination-research-dataset-public-pair), and 

the proprietary application dataset with the internal USPTO version of PatEx (PALM) that 

contains information on non-public applications. From these datasets, we extracted all the 

applications, granted or pending, with examiner office actions of the following types: non-final 

rejection, final rejection, ex parte quayle, and notice of allowance. The earliest mailing date 

shown in PatEx for an application was defined as the “first office action.” 

The next step for the public dataset was to merge the patent applications from PatEx with the 

USPTO Office Action Research Dataset for Patents (OADP) described in Lu et al. 2017 (available 

at https://www.uspto.gov/learning-and-resources/electronic-data-products/office-actionresearch-dataset-patents). OADP provides comprehensive information on examiner issued 

rejections at all stages of prosecution. It covers office actions mailed during the period 2008 to 

mid-2017 and indicates whether the examiners’ office actions contain a non-final or final 

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rejection along with a description of the rejection types. The rejection type of interest is 35 USC 

§101 (Section 101) as it indicates a rejection for patent-ineligible subject matter. Note that each

office action may have multiple rejections and rejection types. OADP was merged with PatEx

using the patent application numbers and the office action mailing dates.

For the proprietary dataset, we merged a proprietary version of the OADP to obtain information 

on office actions for non-public applications. For both public and private applications, PALM 

identified the examiner who issued the first office action. PatEx only identifies the last examiner 

associated with the patent application and, in many cases, that person is not the one who issued 

the first office action. 

Description of patent application characteristics 

We added several characteristics to the application-level dataset. For each application, when 

available, we added the U.S. Patent Classification (USPC) listed on the pre-grant publication 

(PGPub). The classification listed on the PGPub is most likely the classification at the time of the 

examiner’s first office action. For granted patents without a PGPub, we used the USPC listed on 

the granted patents. For a small number of applications that were published after USPTO 

discontinued the USPC system in 2015, we had to use the most recent USPC from PatEx. Lastly, 

for unpublished applications, we used the latest classification as listed in the PALM system. 

For the public dataset, we augmented the application information with text-based data 

elements computed from published and granted patents. From the Patent Claims Research 

Dataset described in Marco et al. 2019 we added a variable to capture claim scope (the 

“breadth” of a claimed invention) by using the independent claim count (ICC) and shortest 

independent claim (ICL). (That dataset is available at https://www.uspto.gov/learning-andresources/electronic-data-products/patent-claims-research-dataset.) Next, we developed a textbased measure of abstract language in patent claims (details available upon request). The source 

for patent application claims text is the USPTO bulk XML data (https://bulkdata.uspto.gov/). For 

each patent application, we used the text in the PGPub (when available) or the text in the 

granted patent. Because these text-based data elements rely on publicly available information, 

they are not available for our proprietary dataset used to evaluate the Berkheimer Memorandum 

and the 2019 PEG. 

The public and proprietary patent application-level datasets are pooled cross-sections of 

applications grouped into one month cohorts by filing date (i.e., application-filing month 

observations). These datasets were used to estimate the models for the likelihood of receiving a 

first office action with a rejection for patent-ineligible subject matter. 

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Datasets at the technology class level 

To evaluate the degree of uncertainty in the patent examination process, we constructed two 

new datasets. In these datasets, the application-level observations were aggregated to broader 

technology classes (3-digit USPCs) and the time dimension was expanded to cover six month 

intervals (i.e., technology-half year observations). These adjustments were necessary in order to 

measure the degree of variation across examiners in their decision-making outcomes. The 

decision outcomes, which are described in detail in the next section, are each examiner’s 

proportion of first office actions with rejections for patent-ineligible subject matter. We 

calculated the variance across examiners in their decision outcomes within a technology area 

and time period (i.e., the variance across examiners within technology-half year cells). 

For the other data elements, when those are available in the public and private application 

datasets, we used aggregated values calculated by taking their average for the technology-half 

year cells. For instance, for counts of independent claims in the applications, the values are the 

average number of independent claims among applications filed in a particular 3-digit USPC and 

specific half-year time period. We also added a new data element: the average of the variance 

across examiners in their decision outcomes. When included in the models, this “level of 

variance” in the technology-half year cells helps to identify that portion of changes in 

uncertainty attributable to examiner decisions about the event under study such as the Alice 

decision. Changes in uncertainty within USPC 3-digit technology areas might reflect examiner 

compositional changes such as the adding of new examiners with less experience or changes in 

how applications are assigned to examiners. It would be wrong to attribute this type of influence 

to the event under study. Some other new data elements include the number of examiners and 

the number of applications per technology-half year. 

The public and proprietary technology-half year datasets are panels with multiple observations 

for each technology class. These datasets were used to estimate the models for examination 

uncertainty in the first office action stage of patent examination. 

Restrict to applications filed before Alice, Berkheimer memorandum and USPTO 2019 

PEG 

A potential source of endogeneity for identifying the impact of Alice and subsequent guidance 

documents on examiner outcomes is applicant behavior. In particular, applicants may react to 

the Alice decision and guidance documents by submitting redrafted applications, or prosecuting 

applications differently after the events. To alleviate these endogeneity concerns, we restricted

our datasets to applications filed before the relevant event. This restriction mitigates the 

influence of applicant drafting strategies on our results. In a robustness check, we explored the 

implications of this restriction. 

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Additionally, our outcomes of interest are at the first office action stage of patent examination. 

Subsequent examination stages involve additional decision makers such as attorneys, agents, 

supervisors, and so forth. Thus, using a later stage of patent examination would confound the 

decisions of examiners with many others. Further, the first office action describes the initial 

patentability decision by the examiner and is performed with minimal interaction with 

applicants, which further reduces the influence of applicant behavior on the examination 

outcomes in this study. 

Restriction to adjust for the Supreme Court Case Bilski v. Kappos 

In 2010, the U.S. Supreme Court decided Bilski v. Kappos (Bilski), which was another case 

involving a business method patent. The Court found the claims to be patent-ineligible because 

they were directed toward an abstract idea. For this analysis, the Bilski decision may affect the 

pre-event trends (e.g., the trends before the Alice decision) in the Alice-affected technologies 

and other technologies. As discusses below, the empirical design of this study requires a 

statistically indistinguishable pre-event trend in Alice-affected and other technologies. To 

address this potential issue, the regression dataset starts in 2013 instead of 2011, thereby 

minimizing any impact from the Bilski decision. In a robustness check of the results, we relaxed 

this restriction and used the data from 2011. The results are quantitatively very similar and 

qualitatively the same. 

Definition of “Alice-affected” and other technology groups 

To identify the technologies affected by Alice (called Alice-affected technologies), we drew on 

patent litigation cases where validity was challenged under the “abstract idea” judicial exception. 

The USPTO’s Office of Patent Legal Administration (OPLA) maintains a public and 

comprehensive list of cases from the U.S. Court of Appeals for the Federal Circuit (CAFC) and the 

U.S. Supreme Court. A team of lawyers monitors these cases and identifies patents litigated for 

subject matter eligibility. One of the fields recorded for each case is the “Exception Type.” This 

refers to the relevant judicial exception type (i.e. laws of nature, products of nature, natural 

phenomena, or abstract ideas). 

For this analysis, we defined Alice-affected technologies as USPCs appearing in cases involving 

the abstract ideas judicial exception. This group of technologies has 33 USPCs and comprises 

33% of the patent applications in our dataset. The group of other technologies includes all 

USPCs that were not involved in any decision from the cases considered by OPLA and contains 

applications from 382 different USPCs. 

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(101);, = /30 + /3,(PatGrp;, • Alice,) + o. + f!j + r, + aX;, + E;, (1) 

 

Outcome variable definitions 

Percentage of first office action Section 101 rejections 

Our first outcome of interest is the likelihood of receiving a first office action with a rejection for 

patent-ineligible subject matter (also referred to as a Section 101 rejection). This is a binary 

indicator that takes the value of 1 when the first office action includes a rejection for patentineligible subject matter, 0 otherwise. The binary indicator is the dependent variable in the 

regression models, but the descriptive figures shown in the Adjusting to Alice report aggregated 

this indicator by technology and month. Specifically, those figures show the percentage of first 

office action Section 101 rejections among all first office actions in the technology groups of 

interest, Alice-affected and other technologies, for a given month. 

Section 101 first action examination uncertainty 

The second outcome measure captures one source of uncertainty in the patent examination 

process. As described above, the analysis focused on uncertainty surrounding examiners’ 

decisions in the first action stage of patent examination. The uncertainty metric was computed 

in two steps. First, within technology-half year cells, the first office actions for each application 

were aggregated to the examiner-level. This allowed us to calculate the share of first office 

action Section 101 rejections for each examiner. Next, the variance across the examiners’ shares 

was used to capture uncertainty. This metric will increase when examiners’ determinations on 

subject matter eligibility are more uneven within a technology-half year. 

For our descriptive analysis in the Adjusting to Alice report, Section 101 first office action 

examination uncertainty is the average of the variance observed in each technology-half year for 

the groups of interest, Alice-affected and other technologies. 

Methodology 

Alice effect on the probability of first office action Section 101 rejections 

To identify the impact of Alice on the likelihood of receiving a first office action with a rejection 

for patent-ineligible subject matter, we used a difference-in-differences methodology with a

linear probability model. The baseline estimating equation (see Table 2 in the Findings Section) 

is: 

where ii indicates the patent application, jj is the examiner, kk is the technology area (3-digit 

USPC), tt is time, pppp(101)iiii is a binary variable that indicates whether the application received a 

Section 101 rejection on the first action, δδkk is a technology fixed effect, γγii is a time fixed effect, 

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pr(101)1t = Po+ /31 (PatGrp., •Alice,) + /3,Abst; + ok + 8i + y, + aXi, + <it (2) 

pr(101)1t = Po+ /31 (Pa.tGrpi, •Alice,) + /3,Absti + /3,(PatGrpi, • Absti • Alice,) + o. (3) 

+ 8j + r, + x i,a + <it 

θθjj is an examiner fixed effect, and XXiiii is a vector of patent characteristics. Alice-affected 

technologies are represented by the variable, PPPPttPPppppiiii, and the event indicator for the Alice 

decision is captured by AAAAiiccccii. The event indicator takes the value of 0 before June 2014 and the 

value 1 thereafter. We used robust standard errors to account for heteroskedasticity. The 

coefficient, ββ1, is the “treatment” effect. The magnitude and statistical significance of this 

coefficient reveals how the Alice decision changed the probability of receiving a first office 

action with a Section 101 rejection. The impact of the Alice decision is evaluated for those 

technologies exposed to the Alice standard (i.e., Alice-affected technologies in PPPPttPPppppiiii) relative 

to the other technologies. This relative comparison eliminates common influences affecting all 

technologies such as an increase in Section 101 rejections due to utility and statutory double 

patenting. 

Additional co-variates for non-random assignment 

Even though the Alice decision is plausibly exogenous to the USPTO and its examiners, the 

assignment of patent applications to the Alice-affected group is not random because it relies on 

litigation events and the technologies of the patents involved. The use of OPLA’s information on 

litigation cases resulted in a broad definition of technologies potentially impacted by Alice. This 

is a conservative way to define the “treatment” group for the analysis. However, the model 

specifications also include a variety of patent application characteristics, XXiiii, such as claim scope 

to help alleviate concerns of possible omitted co-variates. Due to the nature of the Alice 

decision, we also explored whether the degree of abstractness contained in the language of the 

patent application was a confounding influence. We estimated a specification that includes the 

abstractness variable AAAAAAttii for application ii as follows (see Table 2 in the Findings Section): 

Differential Alice impact due to abstract language 

We also explored the possibility that the Alice decision impacted Alice-affected technologies 

differentially according to the degree of abstract language contained in the patent applications. 

To do this we added the following interaction term to the model: (PPPPttPPppppiiii∗ AAAAAAttii ∗ AAAAiiccccii

). The 

empirical specification becomes (see Table 2 in the Findings Section): 

With this model, the impact of the Alice decision or “treatment effect” is: ββ1 + ββ3 ∗ AAAAAAttii. If ββ3 is 

positive and statistically significant, then the Alice decision caused an additional increase in the 

likelihood of receiving Section 101 for those applications containing more abstract language in 

the group of Alice-affected technologies. 

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2012-20 1& 

pr(l Ol ),r = Po + L P,(PatGrp,, • Month,) + ,P,Abst1 + 8k + 91 + X,,a + ,,, (4 ) 

' 

(5) 

Assessment of pre-trends 

One of the key assumptions required for identifying causal effects with a difference-indifference research design is that the pre-event trends in Alice-affected and other technologies 

are the same (or at least statistically indistinguishable). To verify this assumption, we estimated 

a model that interacts an indicator variable for Alice-affected technologies, PPPPttPPppppiiii, and a 

monthly time indicator, MMMMMMtthii, that begins in January 2013 and runs through December of 

2016. For the pre-event trends assumption to hold, the coefficient estimates on the interaction 

terms should not be statistically different from zero before the Alice decision and should be 

statistically different after the Alice decision. The estimation equation is given by (see Figure 1 in 

the Findings Section): 

Berkheimer memo and 2019 PEG effects on the probability of first office action 

Section 101 rejections 

To assess the effects of the Berkheimer memorandum and the 2019 PEG on the likelihood of 

receiving a first office action with a rejection for patent-ineligible subject matter, we used the 

same modeling approach described for the Alice decision. The event indicator for the 

Berkheimer memorandum takes the value of 0 before April 2018, and 1 thereafter. For the 2019

PEG, the event indicator takes the value of 0 before January 2019, and 1 thereafter. These event 

indicators are interacted with the Alice-affected technologies. However, the model specifications 

for these two events are more parsimonious. Due to the limitations of the proprietary patent 

application dataset, the specifications did not include application-level co-variates such as claim 

scope and application language abstractness. 

Alice effect on first action examination uncertainty 

To identify the impact of Alice on first action examination uncertainty, we use the difference-indifference model given by (see Table 3 in the Findings Section): 

where kk indicates the technology area, tt is time in half-year intervals, δδkk is a technology (USPC) 

fixed effect, γγii is a half-year fixed effect, AAAAiiccccii is an event indicator variable taking a value of 1 

after the Alice decision, and XXkkii is a vector of USPC-level by time period co-variates. PPPPttPPppppiiii

indicates whether the application is part of the Alice-affected technologies treatment group. The 

dependent variable VVPPppkkii is the variance of Section 101 rejection rates across examiners in 

technology kk in period tt. That variable measures the decision-making spread in the 

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determinations of Section 101 rejections by examiners and, all else equal, greater variation 

reflects greater uncertainty about whether the patent application will receive a rejection for 

patent-ineligible subject matter in the first action stage of examination. 

In the specification shown in eq. (5), we included a variety of patent application characteristics 

XXiiii averaged across the applications within each technology-half year cell. Besides the patent 

claim scope co-variates used in the models for Section 101 rejections, we added the variance in 

the abstractness of the language in the applications that examiners’ received within each 

technology-half year cell. If examiners were assigned applications with varying degrees of 

abstract language after the Alice decision, Section 101 rejection decision variance may increase 

mechanically rather than reflect decision-making uncertainty from Alice. The abstractness covariate addresses this potential issue. 

We added several more co-variates to capture other sources of increased variance that may not 

be attributable to the Alice decision. These include within-examiner Section 101 rejection rate 

variances (the standard errors of each examiner’s Section 101 rejection rate affects the variance 

across examiners), the number of examiners, and applications per examiner within each USPC at 

time tt (because the number of examiners and applications affects the variance across 

examiners). These controls may be important as our dataset restricts the applications to those 

filed before Alice, which was done as a way to mitigate any influence from applicant behavior. 

This dataset restriction means that the number of first office actions will decrease as time passes 

because more and more of those applications have received a first office action decision. Such a

mechanistic decrease could lead to an increase in the variance across examiners that does not 

reflect Alice. 

Additional co-variates for non-random assignment 

Similar to the specification for Section 101 rejections, we estimated a second model that 

includes the average abstractness measure, AAAAAA_AAAAAAttii. This co-variate is the average 

abstractness of the patent applications receiving a first office action in the USPC at time tt. The 

coefficient will capture any direct effect of abstract language on examination uncertainty. 

Differential Alice impact due to abstract language 

We also explored the possibility that the Alice decision impacted first action examination 

uncertainty differentially according to the degree of abstract language contained in the patent 

applications. The approach is the same as discussed above for Section 101 rejections. 

Specifically, in a third model, we added a triple interaction term. 

Assessment of pre-trends 

To verify that the pre-event trends in examination uncertainty for Alice-affected and other 

technologies are statistically indistinguishable, we estimated a model that interacts an indicator 

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013-20 16 

Var,., = /30 + L /3,(PatGrp" • Ha.l[Yea.r,) + ,JJ1Avg_Abst, +a. + 8j + X"a + E;, 

(6) 

variable for Alice-affected technologies, PPPPttPPppppiiii, with a half-year time indicator, HHPPAAHHHHccPPppii, that 

begins in January 2013 and runs through December of 2016. For the pre-event trends 

assumption to hold, the coefficient estimates on the interaction terms should not be statistically 

different from zero before the Alice decision. The estimation equation is given by (see Figure 2 

in the Findings Section): 

Berkheimer memo and 2019 PEG effects on first action examination uncertainty 

Once again, we used a similar approach to estimate the effects of the Berkheimer memorandum 

and the 2019 PEG on first action examination uncertainty. The event indicators are defined as 

before and included in the regression models. There are data limitations from using the 

proprietary dataset. For instance, we could not include several application characteristics such as 

average abstractness, average claim scope (both ICL and ICC), and the variance in abstractness 

across examiners. Despite this, we were able to include fixed effects, the number of applications, 

and the number of applications per examiner to alleviate concerns related to our identifying 

assumptions. 

Findings 

Descriptive analysis 

The figures that descriptively illustrate the findings are presented the Adjusting to Alice report. 

Table 1 provides the mean values and standard deviations for three groups – other 

technologies, Alice-affected technologies, and combined – for the public dataset, before and 

after the Alice decision. Alice-affected technologies have a higher degree of abstractness 

compared to other technologies, but the Alice decision did not change the degree of 

abstractness. For instance, abstractness for Alice-affected technologies did not change much 

after Alice (0.02416 to 0.02419). This is desired and reflects the dataset restriction to only those 

applications filed before the Alice decision. The outcomes of interest – the percentage of first 

office actions with a Section 101 rejection and uncertainty in first action examination – both 

increased for Alice-affected and other technologies; however, the increase in the Alice-affected 

technologies is substantially larger. These differences anticipate the regression findings 

presented below. 

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Table 1: Summary Statistics 

Other technologies Alice-affected 

technologies 

Combined 

mean std dev mean std dev mean std dev 

Application Abstractness 0.01679 0.0093403 0.02418 0.012319 0.01923 0.01098 

Application Abstractness 

[before Alice] 

0.01682 0.0093729 0.02416 0.012343 0.01918 0.01097 

Application Abstractness 

[after Alice] 

0.01676 0.0093095 0.02419 0.0123 0.01927 0.011 

Percent of § 101 

Rejections [before Alice] 

5.42% 22.63% 21.11% 40.81% 10.45% 30.60% 

Percent of § 101 

Rejections [after Alice] 

6.18% 24.08% 28.34% 45.07% 13.68% 34.37% 

§ 101 Examination Variance

[before Alice] 

0.02665 0.0454264 0.09105 0.044542 0.03204 0.04872 

§ 101 Examination Variance

[after Alice]

0.02984 0.0547089 0.12266 0.065881 0.03808 0.06171 

Regression results 

Alice effect on first office action Section 101 rejections 

Table 2 presents the main regression findings for the impact of the Alice decision on the 

likelihood of receiving a first office action with a rejection for patent-ineligible subject matter. 

Model 1 shows the coefficient estimates for the baseline specification (eq. 1). The coefficient, ββ1, 

on the interaction term (AAAAiicccc ∗ PPPPttPPppppiiii) is 0.0466, and is statistically significant at the 1% level. 

The result shows that the Alice decision increased the probability of receiving a Section 101 

rejection in the first office action by 4.66 percentage points for a patent application in Aliceaffected technologies. 

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Table 2: Regression Results for the Alice Decision on Section 101 Rejections 

VARIABLES Model 1 Model 2 Model 3 

Abst 

Alice x PatGrp 

Alice x PatGrp x Abst 

ICC 

ICL 

Constant 

Observations 

R-squared

Time FE

Technology FE

Examiner FE

0.0466*** 

(0.00148) 

0.00927*** 

(0.000284) 

-7.18e-05***

(3.73e-06)

0.0909***

(0.00835)

1,027,924 

0.308 

Month x Year 

USPC 

Yes 

0.528*** 

(0.0341) 

0.0467*** 

(0.00148) 

0.00915*** 

(0.000283) 

-7.77e-05***

(3.83e-06)

0.0830***

(0.00836)

1,027,924 

0.309 

Month x Year 

USPC 

Yes 

0.186*** 

(0.0362) 

0.0140*** 

(0.00236) 

1.364*** 

(0.0837) 

0.00918*** 

(0.000284) 

-7.57e-05***

(3.82e-06)

0.0879***

(0.00836)

1,027,924 

0.309 

Month x Year 

USPC 

Yes 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

Model 2 contains the application-level abstractness measure as in eq. (2). The coefficient on our 

main variable of interest, (Alice x PatGrp), remains unchanged. The estimate for the abstractness 

term is 0.528, indicating that a one standard deviation increase in the abstractness measure 

leads to a .58 percentage point increase in the probability of receiving a § 101 rejection.1 

Model 3 reports the results from the specification that contains the triple interaction term 

between the treatment group, the abstractness measure, and the Alice event indicator (eq. 3). 

The triple interaction term is statistically significant with a coefficient of 1.364. Using the 

standard deviation from the after-Alice abstractness for applications in Alice-affected 

technologies from Table 1 (i.e., 0.0123), the estimate indicates that the Alice decision had a 

1 This is calculated using the coefficient estimate from Table 2 and the standard deviation of the abstractness measure 

for all USPCs in Table 1, (0.01098*0.528). 

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stronger effect on applications with more abstract language. The probability of receiving a first 

office action with a rejection for patent-ineligible subject matter increased by 1.7 percentage 

points (i.e., 1.364*0.0123) for an application with one standard deviation higher degree of 

abstract language. 

Note that the coefficient on (Alice x PatGrp) remains statistically significant at the 1% level with a 

coefficient estimate of 0.014. With both interaction terms positive and significant, the evidence 

suggests that the Alice decision had its impact on Alice-affected technologies through two 

channels, a direct effect on the Alice-affected technologies and a further effect when these 

patent applications contained more abstract language. The combined effect from the Alice 

decision, when evaluated at the mean abstraction in the Alice-affected group, is 4.7 percentage 

points (ββ1 + ββ3 ∗ .0241896) and is significant at the 1% level. 

Figure 1 displays the point estimates for the interactions terms between Alice-affected 

technologies and the monthly time indicators,(PPPPttPPppppiiii ∗ MMMMMMtthii). The base is the month 

immediately before the Alice decision, May 2014. An F-test for the null hypotheses that all the 

pre-Alice interaction terms are significantly different from zero is rejected at the 1% level. Two 

months, September and October 2013, differ slightly while all the other months are not 

statistically different from zero using 95% confidence interval. This evidence supports the pretrends assumption for the difference-in-difference design. Also apparent is a sharp increase in 

the probability of a Section 101 rejection in the period immediately following Alice (July 2014). 

Using this model, relative to the other technologies, the probability of receiving a first office 

action with a Section 101 rejection increased by 31% through December 2015.2 

2 This is computed in the following way. Let tt1 be the average probability of receiving a Section 101 rejection in the 

treatment group in May 2014, and tt2 be the average probability of receiving a Section 101 rejection in the treatment 

group in December 2015. cc1 and cc2 are the averages for the control group in these two periods. Then, 31 percent is 

(ii2−ii1)−(cc2−cc1) 

∗ 100. ii1

13 
 

 

 

 

 

 

 

 

 

 

 

 

0 

..._'!i 

'1,(;) 

<:-· )'Ir 

Marginal probability of a §101 rejection 

Alice-affected technologies relative to other technologies 

..._'!i ..._!>. ..._!>. ,~ ,~ 

'1,(;) c£l '1,(;) '1,(;) '1,(;) 

)-s-.::., <:-· )-s-.::., )'Ir<:-· )-s-.::., )'b' 

Coefficient on Month Year Treatment 

Figure 1: Monthly estimates for the marginal increase in Section101 rejection rates in 

Alice-affected technologies relative to the control technologies (the base period is May 

2014). The figure shows a clear change post-Alice, and that the pre-trend assumption of 

constant pre-trends is satisfied for one and a half years before Alice. 

Berkheimer memo and 2019 PEG effects on first office action Section 101 rejections 

Table 3 shows the regression results for the effects of the Berkheimer memorandum and the 

2019 PEG on the likelihood of receiving a first office action with a rejection for patent-ineligible 

subject matter. For comparison, Model 1 in column (2) presents the results previously discussed 

in Table 2 for the Alice decision. In the next column, Model 5 shows that the Berkheimer 

memorandum reduced the likelihood of receiving a first office action Section 101 rejection for 

Alice-affected technologies. The estimate is -0.0157 and is statistically significant at the 1% level. 

It indicates the Berkheimer memo reduced the probability by 1.57 percentage points. 

Model 6 shows that the 2019 PEG is associated with a larger decrease in the probability of 

receiving a first office action with a rejection for patent-ineligible subject matter. The coefficient 

estimate is over seven times larger at -0.116 and is significant at the 1% level. Using a model 

similar to eq. 4, the 2019 PEG reduced the likelihood of receiving a first office action with a 

Section 101 rejection by 25% for Alice-affected technologies between Dec. 2019 and Jan. 2020.3 

3 Although equation (4) contains application level co-variates, for reasons described earlier, we do not use these 

independent variables for this estimate. 

14 
 

 

 

 

 

 

Table 3: Regression Results for the Berkheimer Memo and 2019 PEG on Section 101 Rejections 

VARIABLES Model 1 Model 4 Model 5 

Alice x PatGrp 

Berkheimer x PatGrp 

2019 PEG x PatGrp 

ICC 

ICL 

Constant 

Observations 

R-squared

Time FE 

Technology FE 

Examiner FE 

0.0466*** 

(0.00148) 

0.00927*** 

(0.000284) 

-7.18e-05***

(3.73e-06)

0.0909***

(0.00835)

1,027,924

0.308 

Month x Year 

USPC 

Yes 

-0.0157***

(0.00179)

0.137*** 

(0.0117) 

703,222 

0.439 

Month x Year 

USPC 

Yes 

-0.116***

(0.00144)

0.137*** 

(0.00992) 

1,047,734 

0.387 

Month x Year 

USPC 

Yes 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

Alice effect on first action examination uncertainty 

Table 4 presents the results for the effect of the Alice decision on first action examination 

uncertainty. In Model 6 the variable of interest is the interaction term between the Alice decision 

indicator and the group of Alice-affected technologies from eq. 5, Alice x PatGrp. The coefficient 

estimate is positive and statistically significant at the 1% level. For Alice-affected technologies, 

the size of the estimate indicates that the variance across examiners in Section 101 

determinations increased by roughly 61% using the standard deviation observed before the 

Alice decision (information from Table 1, 61%=0.0272/0.044542*100). 

15 
 

 

 

 

 

 

Table 4: Regression Results for the Alice Decision on Examination Uncertainty 

VARIABLES Model 6 Model 7 Model 8 

Avg. abstractness 

Alice x PatGrp 

Alice x PatGrp x Avg. abstractness 

Avg. number of independent claims 

Avg. word count in claims 

Avg. examiner variance 

Number of examiners 

Number of applications 

Avg. applications per examiner 

Variance in abstractness 

Constant 

Observations 

R-squared

Time FE

Technology FE

0.0272*** 

(0.00453) 

-0.00409

(0.00465)

-9.23e-05

(8.29e-05)

0.144* 

(0.0826) 

-0.000105***

(3.32e-05)

8.36e-06**

(3.77e-06)

-0.00350***

(0.000780)

-43.60

(54.07)

0.0791*** 

(0.0179) 

3,030 

0.551 

Half Year 

USPC 

-1.446

(0.946)

0.0272*** 

(0.00447) 

-0.00329

(0.00450)

-7.50e-05

(8.47e-05)

0.148* 

(0.0809) 

-0.000105***

(3.29e-05)

8.45e-06**

(3.77e-06)

-0.00348***

(0.000775)

-20.96

(49.25)

0.0977*** 

(0.0206) 

3,030 

0.552 

Half Year 

USPC 

-1.458

(0.894)

0.0218 

(0.0473) 

0.230 

(1.947) 

-0.00325

(0.00438)

-7.45e-05

(8.39e-05)

0.150* 

(0.0837) 

-0.000105***

(3.32e-05)

8.48e-06**

(3.76e-06)

-0.00347***

(0.000776)

-21.08

(49.18)

0.0978*** 

(0.0206) 

3,030 

0.552 

Half Year 

USPC 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

In Model 7, the average abstractness of the patent applications in the technology-half years is 

added as a co-variate. It is not statistically significant and does not have a direct effect on first 

action examination uncertainty. Note that the main variable of interest does not change in 

magnitude or significance. Looking at Model 8 with the triple interaction term, the coefficient 

estimate is not significant. Note that including the triple interaction term dramatically increased 

16 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

~ 

<D 

~ 

"<t" 

~ 

C\J 

~ 

0 

C\J 

~ 

t 

,~ ~' 

<t,<::) 

§101 Examination Uncertainty 

Alice-affected technologies relative to other technologies 

t 

t t t t 

it, ,~ 

rt' 

Base Half Year is 2014h1 

the standard error for Alice x PatGrp and produced a statistically insignificant result. That does 

not mean that our main variable no longer holds, rather it reflects multicollinearity and indicates 

that Model 3 is not appropriate. We tested the significance of the overall effect using the mean 

of the abstractness co-variate, (ββ1 + ββ3 ∗ .0234711). It is significant and the main effect remains 

at 0.0272. 

Also of interest are the variance controls in the regressions. As expected, the average number of 

applications per examiner reduced the variation in Section 101 determinations across examiners. 

Also as expected, average examiner variance for their own Section 101 determinations (i.e., 

within-examiner) is associated with greater first action examination uncertainty. The variance of 

abstractness from incoming applications is insignificant, which is consistent with the findings for 

the other application language abstractness co-variates. 

Figure 2 is a coefficient plot from eq. (6) that shows the interactions between the Alice-affected 

technologies group and the half-year time indicators. The base period is the half year directly 

preceding Alice (the first half of 2014). There is a substantial and persistent increase immediately 

after Alice. Further, the pre-trend assumption is satisfied throughout the pre-event period (none 

of the pre-Alice coefficients are statistically different from zero). Using this model and relative to 

the other technologies, first action examination uncertainty for Alice-affected technologies 

increased by 26% by December 2015.4 

4 This computation is identical to that described in the percentage change footnote in the previous section. 

17 
 

 

 

 

 

 

 

 

 

Figure 2: Examination uncertainty in Alice-affected technologies relative to other 

technologies. This figure shows the coefficients for the treatment group interacted with 

each half year time period in model (8). Importantly, the pre-trend is statistically 

indistinguishable from zero, where the base period is the first half of 2014. Further, 

examiner uncertainty increases in the Alice-affected technologies post Alice. 

Berkheimer memo and 2019 PEG effects on first action examination uncertainty 

Table 5 shows the regression results for the effects of the Berkheimer memorandum and the 

2019 PEG on first action examination uncertainty. For comparison, Model 6 in column (2) 

presents the results previously discussed for the Alice decision from Table 4. Model 9, in the next 

column, shows that the Berkheimer memorandum had a negative, but statistically insignificant 

effect on first action examination uncertainty. This means that Berkheimer memorandum did not 

have a statistically distinct effect on Alice-affected technologies relative to other technologies. 

Model 10 in Table 5 shows that the 2019 PEG reduced first action examination uncertainty. The 

coefficient is negative and significant at the 1% level. Using a model with time period 

interactions with the Alice-affected technologies (similar to the model in equation (6)), relative 

to the other technologies, first action examination uncertainty decreased by 44% in the 12 

months following the issuance of the 2019 PEG. 

18 
 

 

 

Table 5: Regression Results for the Berkheimer memo and 2019 PEG on 

Examination Uncertainty 

VARIABLES Model 6 Model 9 Model 10 

Alice x PatGrp 

Berkheimer x PatGrp 

2019 PEG x PatGrp 

Avg. number of independent claims 

Avg. word count in claims 

Avg. examiner variance 

Number of examiners 

Number of applications 

Avg. applications per examiner 

Variance in abstractness 

Constant 

Observations 

R-squared

Time FE

Technology FE

0.0272*** 

(0.00453) 

-0.00409

(0.00465)

-9.23e-05

(8.29e-05)

0.144* 

(0.0826) 

-0.000105***

(3.32e-05)

8.36e-06**

(3.77e-06)

-0.00350***

(0.000780)

-43.60

(54.07)

0.0791*** 

(0.0179) 

3,030 

0.551 

Half Year 

USPC 

-0.00742

(0.00521)

0.0995 

(0.0710) 

-7.45e-05**

(3.25e-05)

7.40e-06

(6.01e-06)

-0.00185***

(0.000596)

0.0509*** 

(0.00425) 

2,361 

0.645 

Half Year 

USPC 

-0.0369***

(0.00473)

0.139** 

(0.0686) 

-7.28e-05***

(2.23e-05)

1.08e-06

(3.49e-06)

-0.000616**

(0.000287)

0.0368*** 

(0.00301) 

2,747 

0.605 

Half Year 

USPC 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

19 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Robustness checks for the impact of Alice 

Assessing dataset restrictions 

The analysis focused on the first action stage of patent examination and restricted the datasets 

to only those patent applications filed before the events of interest in order to minimize the 

influence of other decision makers on our results. By limiting the datasets to applications filed 

before the Alice decision, we induced a mechanical drop in the number of observations over 

time – as examiners completed first office actions the remaining pool of applications awaiting a

first office action shrinks. This is potentially problematic for our estimates of first action 

examination uncertainty since fewer applications with first actions will artificially increase the 

variance across examiners in a way that does not reflect variation in decision-making. We added 

several co-variates to help capture part of this effect such as the number of applications, the 

number of applications per examiner, and a flexible time trend using indicator variables. 

Figure 3 illustrates the implications of the dataset restriction. It shows the percentage of first 

office action non-final and final rejections in our dataset relative to the total number of first 

action rejections in each period. The left censoring reflects the construction of the USPTO Office 

Action Research Dataset for Patents dataset. It includes patent applications with series numbers 

that start with 12, 13, 14 and 15, rather including all applications in a fixed period time (see 

https://www.uspto.gov/patents-application-process/checking-application-status/searchapplication). In figure 3, the vertical bar shows the month of the Alice decision, June 2014. As 

expected, the percentage of first action rejections in our dataset starts to decrease immediately. 

By December 2015, the percentage had decreased to approximately 45%. 

To test the sensitivity of our results on examination uncertainty shown in Table 4, we reestimated the regression models using the time window of 2013 through 2015. Table 6, Models 

11 and 12, shows that the effect of the Alice decision on first action examination uncertainty is 

slightly smaller in magnitude, but statistically significant at the 1% level. Unlike in Table 4, the 

triple interaction term in Model 13 is positive and statistically significant at the 5% level. This 

result suggests that the abstract language content of patent applications further increased first 

action examination uncertainty. The total treatment effect is .018 (ββ1 + ββ3 ∗ .0234302)and is

significant at the 1% level. 

20 
0 

(/) 

C 

0 0 

u (X) 

(I) 

-~ 

C 

0 0 

u c:o 

<1l 

~ 

-= 

0 0 

(I) ""'" O> 

<1l 

c 

Q) 

0 0 

Q) N 

a.. 

Alice Supreme Court Decision 

0 

,:,'o 

~ f::-. 

'::?; 

0 

Figure 3: Percentage of first office action rejections in dataset 

21 
 

 

Table 6: Regression Results for the Test of Dataset Restriction on Applications 

VARIABLES Model 11 Model 12 Model 13 

Avg. abstractness 

Alice x PatGrp 

Alice x PatGrp x Avg. abstractness 

Avg. number of independent claims 

Avg. word count in claims 

Avg. examiner variance 

Number of examiners 

Number of applications 

Avg. applications per examiner 

Variance in abstractness 

Constant 

Observations 

R-squared

Time FE

Technology FE

0.0185*** 

(0.00363) 

-0.00509

(0.00584)

-4.23e-05

(9.40e-05)

0.347***

(0.0664) 

7.86e-05** 

(3.88e-05) 

-3.72e-06

(3.57e-06)

-0.000935*

(0.000486)

-16.58

(39.39)

0.0480**

(0.0205)

2,358 

0.633 

Half Year 

USPC 

-0.904

(0.869)

0.0185*** 

(0.00364) 

-0.00483

(0.00575)

-3.15e-05

(9.42e-05)

0.351***

(0.0667) 

7.87e-05** 

(3.87e-05) 

-3.74e-06

(3.56e-06)

-0.000949*

(0.000484)

-7.877

(38.90)

0.0607**

(0.0259)

2,358 

0.634 

Half Year 

USPC 

-0.907

(0.870)

-0.0165

(0.0154)

1.488** 

(0.629) 

-0.00478

(0.00575)

-2.89e-05

(9.43e-05)

0.358***

(0.0667) 

7.06e-05* 

(3.80e-05) 

-3.55e-06

(3.42e-06)

-0.000917*

(0.000483)

-7.518

(38.90)

0.0605**

(0.0259)

2,358 

0.634 

Half Year 

USPC 

Robust standard errors in parentheses 

*** p<0.01, ** p<0.05, * p<0.1 

22 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Assessing the Influence of Supreme Court Case Bilski v. Kappos 

As discussed in the data construction section, the time dimension for this analysis started in 

2013 instead of 2011. This choice was made to minimize any impact from the Bilski decision on 

the pre-event trends for Alice-affected technologies and other technologies. Rather than limiting 

the pre-event period, this robustness check allows for a linear time trend difference between the 

Alice-affected and other technologies to account for the impact of Bilski. Specifically, we re-run 

our specifications with the term tt ∗ PPPPttPPppMMPPppiiiion the sample (2011 – 2016), and assess the 

appropriateness of a linear time trend using equations (4) and (6). 

The regression results, not shown but available upon request, indicate the Alice decision had a

larger effect on the likelihood of receiving a first office action with a Section 101 rejection, 

0.0596 and significant at the 1% level. For first action examination uncertainty, the magnitude 

was almost the same, -0.0241 and significant at the 1% level. The main results hold up to 

expanding the dataset time period to 2011, the earliest dataset date permitted by available data. 

Potential effects of Alice on first office action 102/103/112 rejections 

In the final robustness check, we assess the impact of Alice on the other main rejection types 

issued by examiners in the first office action, including Section 102 (novelty), Section 103 (nonobviousness) and Section 112 (clarity and enablement). Alice should not impact an examiner’s 

use of these other rejection types for two reasons: (1) Alice clarified the law on subject matter 

eligibility (not novelty, non-obviousness or clarity and enablement issues); and (2), examiners are 

trained to issue all grounds of rejection on the first office action under the USPTO’s compact 

prosecution policy.

5 The second point implies that examiners shouldn’t substitute away from 

other rejections when issuing more Section 101 rejections after Alice. 

We estimate equations (4) and (6) with each of the other rejections types. The time period point 

estimates are provided in Figures 4-9 below. Importantly, none of the figures show a statistically 

significant discontinuous jump in the rejection rates after Alice. 

5 A description of the USPTO’s compact prosecution policy is located at: 

https://www.uspto.gov/web/offices/pac/mpep/s2103.html 

23 
 

 

Marginal probability of a § 102 rejection 

Alice-affected technologies relative to other technologies <t 

~ 

CD 

~ ~~~ 

Coefficient on Month Year Treatment 

Marginal probability of a § 103 rejection 

Alice-affected technologies relative to other technologies CD 

~ 

<t 

Figure 4: Regression coefficients for Section 102 rejections 

Figure 5: Regression coefficients for Section 103 rejections 

24 
 

0 

<t 

Marginal probability of a § 112 rejection 

Alice-affected technologies relative to other technologies 

~ ...,___,_~~~~~~~~~~~~~~~~-

0 

N 

,~ ,~ ,0 

rt' rt' rt' <:;,· ,-}~ <:;,· ':>"' J ':>"' 

Coefficient on Month Year Treatment 

§ 102 Examination Uncertainty 

Alice-affected technologies relative to other technologies 

~~----------------- Coefficient on Half Year Treatment 

Base Half Year is 2014h1 

Figure 6: Regression coefficients for Section 112 rejections 

Figure 7: Regression coefficients for Section 102 examination uncertainty 

25 
 

 

0 

0 

§ 103 Examination Uncertainty 

Alice-affected technologies relative to other technologies 

Coefficient on Half Year Treatment 

Base Half Year is 2014h1 

t 

§ 112 Examination Uncertainty 

Alice-affected technologies relative to other technologies 

t j t t t 

Coefficient on Half Year Treatment 

Base Half Year is 2014h1 

Figure 8: Regression coefficients for Section 103 examination uncertainty 

Figure 9: Regression coefficients for Section 112 examination uncertainty 

26 
 

 

 

U.S. patent classifications used in the report 

The following table listed the U.S. Patent Classifications (USPCs) included in the groups defined 

as Alice-affected and other technologies. 

Table 7: U.S. Patent Classifications in Adjusting to Alice report 

USPC 

number 

or range 

(3-digit) 

Alice-affected 

or other 

technology 

group 

Description 

73 Alice-affected MEASURING AND TESTING 

175 Alice-affected BORING OR PENETRATING THE EARTH 

187 Alice-affected ELEVATOR, INDUSTRIAL LIFT TRUCK, OR STATIONARY LIFT FOR 

VEHICLE 

235 Alice-affected REGISTERS 

273 Alice-affected AMUSEMENT DEVICES: GAMES 

320 Alice-affected ELECTRICITY: BATTERY OR CAPACITOR CHARGING OR 

DISCHARGING 

340 Alice-affected COMMUNICATIONS: ELECTRICAL 

342 Alice-affected COMMUNICATIONS: DIRECTIVE RADIO WAVE SYSTEMS AND 

DEVICES (E.G., RADAR, RADIO NAVIGATION) 

27 
343 Alice-affected COMMUNICATIONS: RADIO WAVE ANTENNAS 

345 Alice-affected COMPUTER GRAPHICS PROCESSING AND SELECTIVE VISUAL 

DISPLAY SYSTEMS 

358 Alice-affected FACSIMILE AND STATIC PRESENTATION PROCESSING 

370 Alice-affected MULTIPLEX COMMUNICATIONS 

379 Alice-affected TELEPHONIC COMMUNICATIONS 

380 Alice-affected CRYPTOGRAPHY 

382 Alice-affected IMAGE ANALYSIS 

424 Alice-affected DRUG, BIO-AFFECTING AND BODY TREATING COMPOSITIONS 

434 Alice-affected EDUCATION AND DEMONSTRATION 

435 Alice-affected CHEMISTRY: MOLECULAR BIOLOGY AND MICROBIOLOGY 

436 Alice-affected CHEMISTRY: ANALYTICAL AND IMMUNOLOGICAL TESTING 

28 
455 Alice-affected TELECOMMUNICATIONS 

463 Alice-affected AMUSEMENT DEVICES: GAMES 

564 Alice-affected ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

700 Alice-affected DATA PROCESSING: GENERIC CONTROL SYSTEMS OR SPECIFIC 

APPLICATIONS 

704 Alice-affected 

DATA PROCESSING: SPEECH SIGNAL PROCESSING, LINGUISTICS, 

LANGUAGE TRANSLATION, AND AUDIO 

COMPRESSION/DECOMPRESSION 

705 Alice-affected DATA PROCESSING: FINANCIAL, BUSINESS PRACTICE, 

MANAGEMENT, OR COST/PRICE DETERMINATION 

707 Alice-affected DATA PROCESSING: DATABASE AND FILE MANAGEMENT OR 

DATA STRUCTURES 

708 Alice-affected ELECTRICAL COMPUTERS: ARITHMETIC PROCESSING AND 

CALCULATING 

709 Alice-affected ELECTRICAL COMPUTERS AND DIGITAL PROCESSING SYSTEMS: 

MULTICOMPUTER DATA TRANSFERRING 

711 Alice-affected ELECTRICAL COMPUTERS AND DIGITAL PROCESSING SYSTEMS: 

MEMORY 

713 Alice-affected ELECTRICAL COMPUTERS AND DIGITAL PROCESSING SYSTEMS: 

SUPPORT 

29 
715 Alice-affected 

DATA PROCESSING: PRESENTATION PROCESSING OF 

DOCUMENT, OPERATOR INTERFACE PROCESSING, AND SCREEN 

SAVER DISPLAY PROCESSING 

725 Alice-affected INTERACTIVE VIDEO DISTRIBUTION SYSTEMS 

726 Alice-affected INFORMATION SECURITY 

2 Other 

technologies APPAREL 

4 Other 

technologies BATHS, CLOSETS, SINKS, AND SPITTOONS 

5 Other 

technologies BEDS 

7 Other 

technologies COMPOUND TOOLS 

8 Other 

technologies 

BLEACHING AND DYEING; FLUID TREATMENT AND CHEMICAL 

MODIFICATION OF TEXTILES AND FIBERS 

12 Other 

technologies BOOT AND SHOE MAKING 

14 Other 

technologies BRIDGES 

15 Other 

technologies BRUSHING, SCRUBBING, AND GENERAL CLEANING 

30 
16 Other 

technologies 

MISCELLANEOUS HARDWARE (E.G., BUSHING, CARPET FASTENER, 

CASTER, DOOR CLOSER, PANEL HANGER, ATTACHABLE OR 

ADJUNCT HANDLE, HINGE, WINDOW SASH BALANCE, ETC.) 

19 Other 

technologies TEXTILES: FIBER PREPARATION 

23 Other 

technologies CHEMISTRY: PHYSICAL PROCESSES 

24 Other 

technologies BUCKLES, BUTTONS, CLASPS, ETC 

26 Other 

technologies TEXTILES: CLOTH FINISHING 

27 Other 

technologies UNDERTAKING 

28 Other 

technologies TEXTILES: MANUFACTURING 

30 Other 

technologies CUTLERY 

33 Other 

technologies GEOMETRICAL INSTRUMENTS 

34 Other 

technologies DRYING AND GAS OR VAPOR CONTACT WITH SOLIDS 

36 Other 

technologies BOOTS, SHOES, AND LEGGINGS 

31 
37 Other 

technologies EXCAVATING 

38 Other 

technologies TEXTILES: IRONING OR SMOOTHING 

40 Other 

technologies CARD, PICTURE, OR SIGN EXHIBITING 

42 Other 

technologies FIREARMS 

43 Other 

technologies FISHING, TRAPPING, AND VERMIN DESTROYING 

44 Other 

technologies FUEL AND RELATED COMPOSITIONS 

47 Other 

technologies PLANT HUSBANDRY 

48 Other 

technologies GAS: HEATING AND ILLUMINATING 

49 Other 

technologies MOVABLE OR REMOVABLE CLOSURES 

51 Other 

technologies 

ABRASIVE TOOL MAKING PROCESS, MATERIAL, OR 

COMPOSITION 

52 Other 

technologies STATIC STRUCTURES (E.G., BUILDINGS) 

32 
53 Other 

technologies PACKAGE MAKING 

54 Other 

technologies HARNESS FOR WORKING ANIMAL 

55 Other 

technologies GAS SEPARATION 

56 Other 

technologies HARVESTERS 

57 Other 

technologies TEXTILES: SPINNING, TWISTING, AND TWINING 

59 Other 

technologies CHAIN, STAPLE, AND HORSESHOE MAKING 

60 Other 

technologies POWER PLANTS 

62 Other 

technologies REFRIGERATION 

63 Other 

technologies JEWELRY 

65 Other 

technologies GLASS MANUFACTURING 

66 Other 

technologies TEXTILES: KNITTING 

33 
68 Other 

technologies TEXTILES: FLUID TREATING APPARATUS 

69 Other 

technologies LEATHER MANUFACTURES 

70 Other 

technologies LOCKS 

71 Other 

technologies CHEMISTRY: FERTILIZERS 

72 Other 

technologies METAL DEFORMING 

74 Other 

technologies MACHINE ELEMENT OR MECHANISM 

75 Other 

technologies 

SPECIALIZED METALLURGICAL PROCESSES, COMPOSITIONS FOR 

USE THEREIN, CONSOLIDATED METAL POWDER COMPOSITIONS, 

AND LOOSE METAL PARTICULATE MIXTURES 

76 Other 

technologies METAL TOOLS AND IMPLEMENTS, MAKING 

81 Other 

technologies TOOLS 

82 Other 

technologies TURNING 

83 Other 

technologies CUTTING 

34 
84 Other 

technologies MUSIC 

86 Other 

technologies AMMUNITION AND EXPLOSIVE-CHARGE MAKING 

87 Other 

technologies TEXTILES: BRAIDING, NETTING, AND LACE MAKING 

89 Other 

technologies ORDNANCE 

91 Other 

technologies MOTORS: EXPANSIBLE CHAMBER TYPE 

92 Other 

technologies EXPANSIBLE CHAMBER DEVICES 

95 Other 

technologies GAS SEPARATION: PROCESSES 

96 Other 

technologies GAS SEPARATION: APPARATUS 

99 Other 

technologies FOODS AND BEVERAGES: APPARATUS 

100 Other 

technologies PRESSES 

101 Other 

technologies PRINTING 

35 
 

102 Other 

technologies AMMUNITION AND EXPLOSIVES 

104 Other 

technologies RAILWAYS 

105 Other 

technologies RAILWAY ROLLING STOCK 

106 Other 

technologies COMPOSITIONS: COATING OR PLASTIC 

108 Other 

technologies HORIZONTALLY SUPPORTED PLANAR SURFACES 

109 Other 

technologies SAFES, BANK PROTECTION, OR A RELATED DEVICE 

110 Other 

technologies FURNACES 

111 Other 

technologies PLANTING 

112 Other 

technologies SEWING 

114 Other 

technologies SHIPS 

116 Other 

technologies SIGNALS AND INDICATORS 

36 
117 Other 

technologies 

SINGLE-CRYSTAL, ORIENTED-CRYSTAL, AND EPITAXY GROWTH 

PROCESSES; NON-COATING APPARATUS THEREFOR 

118 Other 

technologies COATING APPARATUS 

119 Other 

technologies ANIMAL HUSBANDRY 

122 Other 

technologies LIQUID HEATERS AND VAPORIZERS 

123 Other 

technologies INTERNAL-COMBUSTION ENGINES 

124 Other 

technologies MECHANICAL GUNS AND PROJECTORS 

125 Other 

technologies STONE WORKING 

126 Other 

technologies STOVES AND FURNACES 

127 Other 

technologies SUGAR, STARCH, AND CARBOHYDRATES 

128 Other 

technologies SURGERY 

131 Other 

technologies TOBACCO 

37 
132 Other 

technologies TOILET 

134 Other 

technologies CLEANING AND LIQUID CONTACT WITH SOLIDS 

135 Other 

technologies TENT, CANOPY, UMBRELLA, OR CANE 

136 Other 

technologies BATTERIES: THERMOELECTRIC AND PHOTOELECTRIC 

137 Other 

technologies FLUID HANDLING 

138 Other 

technologies PIPES AND TUBULAR CONDUITS 

139 Other 

technologies TEXTILES: WEAVING 

140 Other 

technologies WIREWORKING 

141 Other 

technologies 

FLUENT MATERIAL HANDLING, WITH RECEIVER OR RECEIVER 

COACTING MEANS 

142 Other 

technologies WOOD TURNING 

144 Other 

technologies WOODWORKING 

38 
147 Other 

technologies COOPERING 

148 Other 

technologies METAL TREATMENT 

149 Other 

technologies EXPLOSIVE AND THERMIC COMPOSITIONS OR CHARGES 

150 Other 

technologies PURSES, WALLETS, AND PROTECTIVE COVERS 

152 Other 

technologies RESILIENT TIRES AND WHEELS 

156 Other 

technologies 

ADHESIVE BONDING AND MISCELLANEOUS CHEMICAL 

MANUFACTURE 

157 Other 

technologies WHEELWRIGHT MACHINES 

159 Other 

technologies CONCENTRATING EVAPORATORS 

160 Other 

technologies FLEXIBLE OR PORTABLE CLOSURE, PARTITION, OR PANEL 

163 Other 

technologies NEEDLE AND PIN MAKING 

164 Other 

technologies METAL FOUNDING 

39 
165 Other 

technologies HEAT EXCHANGE 

166 Other 

technologies WELLS 

168 Other 

technologies FARRIERY 

169 Other 

technologies FIRE EXTINGUISHERS 

171 Other 

technologies UNEARTHING PLANTS OR BURIED OBJECTS 

172 Other 

technologies EARTH WORKING 

173 Other 

technologies TOOL DRIVING OR IMPACTING 

174 Other 

technologies ELECTRICITY: CONDUCTORS AND INSULATORS 

177 Other 

technologies WEIGHING SCALES 

178 Other 

technologies TELEGRAPHY 

180 Other 

technologies MOTOR VEHICLES 

40 
181 Other 

technologies ACOUSTICS 

182 Other 

technologies FIRE ESCAPE, LADDER, OR SCAFFOLD 

184 Other 

technologies LUBRICATION 

185 Other 

technologies MOTORS: SPRING, WEIGHT, OR ANIMAL POWERED 

186 Other 

technologies MERCHANDISING 

188 Other 

technologies BRAKES 

190 Other 

technologies TRUNKS AND HAND-CARRIED LUGGAGE 

191 Other 

technologies ELECTRICITY: TRANSMISSION TO VEHICLES 

192 Other 

technologies 192 CLUTCHES AND POWER-STOP CONTROL 

193 Other 

technologies CONVEYORS, CHUTES, SKIDS, GUIDES, AND WAYS 

194 Other 

technologies CHECK-ACTUATED CONTROL MECHANISMS 

41 
196 Other 

technologies MINERAL OILS: APPARATUS 

198 Other 

technologies CONVEYORS: POWER-DRIVEN 

200 Other 

technologies ELECTRICITY: CIRCUIT MAKERS AND BREAKERS 

201 Other 

technologies DISTILLATION: PROCESSES, THERMOLYTIC 

202 Other 

technologies DISTILLATION: APPARATUS 

203 Other 

technologies DISTILLATION: PROCESSES, SEPARATORY 

204 Other 

technologies CHEMISTRY: ELECTRICAL AND WAVE ENERGY 

205 Other 

technologies 

ELECTROLYSIS: PROCESSES, COMPOSITIONS USED THEREIN, AND 

METHODS OF PREPARING THE COMPOSITIONS 

206 Other 

technologies SPECIAL RECEPTACLE OR PACKAGE 

208 Other 

technologies MINERAL OILS: PROCESSES AND PRODUCTS 

209 Other 

technologies CLASSIFYING, SEPARATING, AND ASSORTING SOLIDS 

42 
210 Other 

technologies LIQUID PURIFICATION OR SEPARATION 

211 Other 

technologies SUPPORTS: RACKS 

212 Other 

technologies TRAVERSING HOISTS 

213 Other 

technologies RAILWAY DRAFT APPLIANCES 

215 Other 

technologies BOTTLES AND JARS 

216 Other 

technologies ETCHING A SUBSTRATE: PROCESSES 

217 Other 

technologies WOODEN RECEPTACLES 

218 Other 

technologies 

HIGH-VOLTAGE SWITCHES WITH ARC PREVENTING OR 

EXTINGUISHING DEVICES 

219 Other 

technologies ELECTRIC HEATING 

220 Other 

technologies RECEPTACLES 

221 Other 

technologies ARTICLE DISPENSING 

43 
222 Other 

technologies DISPENSING 

223 Other 

technologies APPAREL APPARATUS 

224 Other 

technologies PACKAGE AND ARTICLE CARRIERS 

225 Other 

technologies SEVERING BY TEARING OR BREAKING 

226 Other 

technologies ADVANCING MATERIAL OF INDETERMINATE LENGTH 

227 Other 

technologies ELONGATED-MEMBER-DRIVING APPARATUS 

228 Other 

technologies METAL FUSION BONDING 

229 Other 

technologies ENVELOPES, WRAPPERS, AND PAPERBOARD BOXES 

231 Other 

technologies WHIPS AND WHIP APPARATUS 

232 Other 

technologies DEPOSIT AND COLLECTION RECEPTACLES 

234 Other 

technologies SELECTIVE CUTTING (E.G., PUNCHING) 

44 
236 Other 

technologies AUTOMATIC TEMPERATURE AND HUMIDITY REGULATION 

237 Other 

technologies HEATING SYSTEMS 

238 Other 

technologies RAILWAYS: SURFACE TRACK 

239 Other 

technologies FLUID SPRINKLING, SPRAYING, AND DIFFUSING 

241 Other 

technologies SOLID MATERIAL COMMINUTION OR DISINTEGRATION 

242 Other 

technologies WINDING, TENSIONING, OR GUIDING 

244 Other 

technologies AERONAUTICS AND ASTRONAUTICS 

245 Other 

technologies WIRE FABRICS AND STRUCTURE 

246 Other 

technologies RAILWAY SWITCHES AND SIGNALS 

248 Other 

technologies SUPPORTS 

249 Other 

technologies STATIC MOLDS 

45 
 

250 Other 

technologies RADIANT ENERGY 

251 Other 

technologies VALVES AND VALVE ACTUATION 

252 Other 

technologies COMPOSITIONS 

254 Other 

technologies 

IMPLEMENTS OR APPARATUS FOR APPLYING PUSHING OR 

PULLING FORCE 

256 Other 

technologies FENCES 

257 Other 

technologies 

ACTIVE SOLID-STATE DEVICES (E.G., TRANSISTORS, SOLID-STATE 

DIODES) 

258 Other 

technologies RAILWAY MAIL DELIVERY 

260 Other 

technologies CHEMISTRY OF CARBON COMPOUNDS 

261 Other 

technologies GAS AND LIQUID CONTACT APPARATUS 

264 Other 

technologies 

PLASTIC AND NONMETALLIC ARTICLE SHAPING OR TREATING: 

PROCESSES 

266 Other 

technologies METALLURGICAL APPARATUS 

46 
 

267 Other 

technologies SPRING DEVICES 

269 Other 

technologies WORK HOLDERS 

270 Other 

technologies SHEET-MATERIAL ASSOCIATING 

271 Other 

technologies SHEET FEEDING OR DELIVERING 

277 Other 

technologies SEAL FOR A JOINT OR JUNCTURE 

279 Other 

technologies CHUCKS OR SOCKETS 

280 Other 

technologies LAND VEHICLES 

281 Other 

technologies BOOKS, STRIPS, AND LEAVES 

283 Other 

technologies PRINTED MATTER 

285 Other 

technologies PIPE JOINTS OR COUPLINGS 

289 Other 

technologies KNOTS AND KNOT TYING 

47 
290 Other 

technologies PRIME-MOVER DYNAMO PLANTS 

291 Other 

technologies TRACK SANDERS 

292 Other 

technologies CLOSURE FASTENERS 

293 Other 

technologies VEHICLE FENDERS 

294 Other 

technologies HANDLING: HAND AND HOIST-LINE IMPLEMENTS 

295 Other 

technologies RAILWAY WHEELS AND AXLES 

296 Other 

technologies LAND VEHICLES: BODIES AND TOPS 

297 Other 

technologies CHAIRS AND SEATS 

298 Other 

technologies LAND VEHICLES: DUMPING 

299 Other 

technologies MINING OR IN SITU DISINTEGRATION OF HARD MATERIAL 

300 Other 

technologies BRUSH, BROOM, AND MOP MAKING 

48 
301 Other 

technologies LAND VEHICLES: WHEELS AND AXLES 

303 Other 

technologies FLUID-PRESSURE AND ANALOGOUS BRAKE SYSTEMS 

305 Other 

technologies WHEEL SUBSTITUTES FOR LAND VEHICLES 

307 Other 

technologies ELECTRICAL TRANSMISSION OR INTERCONNECTION SYSTEMS 

310 Other 

technologies ELECTRICAL GENERATOR OR MOTOR STRUCTURE 

312 Other 

technologies SUPPORTS: CABINET STRUCTURE 

313 Other 

technologies ELECTRIC LAMP AND DISCHARGE DEVICES 

315 Other 

technologies ELECTRIC LAMP AND DISCHARGE DEVICES: SYSTEMS 

318 Other 

technologies ELECTRICITY: MOTIVE POWER SYSTEMS 

322 Other 

technologies ELECTRICITY: SINGLE GENERATOR SYSTEMS 

323 Other 

technologies ELECTRICITY: POWER SUPPLY OR REGULATION SYSTEMS 

49 
324 Other 

technologies ELECTRICITY: MEASURING AND TESTING 

326 Other 

technologies ELECTRONIC DIGITAL LOGIC CIRCUITRY 

327 Other 

technologies 

MISCELLANEOUS ACTIVE ELECTRICAL NONLINEAR DEVICES, 

CIRCUITS, AND SYSTEMS 

329 Other 

technologies DEMODULATORS 

330 Other 

technologies AMPLIFIERS 

331 Other 

technologies OSCILLATORS 

332 Other 

technologies MODULATORS 

333 Other 

technologies WAVE TRANSMISSION LINES AND NETWORKS 

334 Other 

technologies TUNERS 

335 Other 

technologies 

ELECTRICITY: MAGNETICALLY OPERATED SWITCHES, MAGNETS, 

AND ELECTROMAGNETS 

336 Other 

technologies INDUCTOR DEVICES 

50 
337 Other 

technologies 

ELECTRICITY: ELECTROTHERMALLY OR THERMALLY ACTUATED 

SWITCHES 

338 Other 

technologies ELECTRICAL RESISTORS 

341 Other 

technologies CODED DATA GENERATION OR CONVERSION 

346 Other 

technologies RECORDERS 

347 Other 

technologies INCREMENTAL PRINTING OF SYMBOLIC INFORMATION 

348 Other 

technologies TELEVISION 

349 Other 

technologies LIQUID CRYSTAL CELLS, ELEMENTS AND SYSTEMS 

351 Other 

technologies OPTICS: EYE EXAMINING, VISION TESTING AND CORRECTING 

352 Other 

technologies OPTICS: MOTION PICTURES 

353 Other 

technologies OPTICS: IMAGE PROJECTORS 

355 Other 

technologies PHOTOCOPYING 

51 
356 Other 

technologies OPTICS: MEASURING AND TESTING 

359 Other 

technologies OPTICAL: SYSTEMS AND ELEMENTS 

360 Other 

technologies DYNAMIC MAGNETIC INFORMATION STORAGE OR RETRIEVAL 

361 Other 

technologies ELECTRICITY: ELECTRICAL SYSTEMS AND DEVICES 

362 Other 

technologies ILLUMINATION 

363 Other 

technologies ELECTRIC POWER CONVERSION SYSTEMS 

365 Other 

technologies STATIC INFORMATION STORAGE AND RETRIEVAL 

366 Other 

technologies AGITATING 

367 Other 

technologies 

COMMUNICATIONS, ELECTRICAL: ACOUSTIC WAVE SYSTEMS 

AND DEVICES 

368 Other 

technologies HOROLOGY: TIME MEASURING SYSTEMS OR DEVICES 

369 Other 

technologies DYNAMIC INFORMATION STORAGE OR RETRIEVAL 

52 
372 Other 

technologies COHERENT LIGHT GENERATORS 

373 Other 

technologies INDUSTRIAL ELECTRIC HEATING FURNACES 

374 Other 

technologies THERMAL MEASURING AND TESTING 

375 Other 

technologies PULSE OR DIGITAL COMMUNICATIONS 

376 Other 

technologies 

INDUCED NUCLEAR REACTIONS: PROCESSES, SYSTEMS, AND 

ELEMENTS 

377 Other 

technologies 

ELECTRICAL PULSE COUNTERS, PULSE DIVIDERS, OR SHIFT 

REGISTERS: CIRCUITS AND SYSTEMS 

378 Other 

technologies X-RAY OR GAMMA RAY SYSTEMS OR DEVICES 

381 Other 

technologies ELECTRICAL AUDIO SIGNAL PROCESSING SYSTEMS AND DEVICES 

383 Other 

technologies FLEXIBLE BAGS 

384 Other 

technologies BEARINGS 

385 Other 

technologies OPTICAL WAVEGUIDES 

53 
 

386 Other 

technologies 

MOTION VIDEO SIGNAL PROCESSING FOR RECORDING OR 

REPRODUCING 

388 Other 

technologies ELECTRICITY: MOTOR CONTROL SYSTEMS 

392 Other 

technologies ELECTRIC RESISTANCE HEATING DEVICES 

396 Other 

technologies PHOTOGRAPHY 

398 Other 

technologies OPTICAL COMMUNICATIONS 

399 Other 

technologies ELECTROPHOTOGRAPHY 

400 Other 

technologies TYPEWRITING MACHINES 

401 Other 

technologies COATING IMPLEMENTS WITH MATERIAL SUPPLY 

402 Other 

technologies 

BINDER DEVICE RELEASABLY ENGAGING APERTURE OR NOTCH 

OF SHEET 

403 Other 

technologies JOINTS AND CONNECTIONS 

404 Other 

technologies ROAD STRUCTURE, PROCESS, OR APPARATUS 

54 
405 Other 

technologies HYDRAULIC AND EARTH ENGINEERING 

406 Other 

technologies CONVEYORS: FLUID CURRENT 

407 Other 

technologies CUTTERS, FOR SHAPING 

408 Other 

technologies CUTTING BY USE OF ROTATING AXIALLY MOVING TOOL 

409 Other 

technologies GEAR CUTTING, MILLING, OR PLANING 

410 Other 

technologies FREIGHT ACCOMMODATION ON FREIGHT CARRIER 

411 Other 

technologies 

EXPANDED, THREADED, DRIVEN, HEADED, TOOL-DEFORMED, OR 

LOCKED-THREADED FASTENER 

412 Other 

technologies BOOKBINDING: PROCESS AND APPARATUS 

413 Other 

technologies SHEET METAL CONTAINER MAKING 

414 Other 

technologies MATERIAL OR ARTICLE HANDLING 

415 Other 

technologies ROTARY KINETIC FLUID MOTORS OR PUMPS 

55 
 

416 Other 

technologies FLUID REACTION SURFACES (I.E., IMPELLERS) 

417 Other 

technologies PUMPS 

418 Other 

technologies ROTARY EXPANSIBLE CHAMBER DEVICES 

419 Other 

technologies POWDER METALLURGY PROCESSES 

420 Other 

technologies ALLOYS OR METALLIC COMPOSITIONS 

422 Other 

technologies 

CHEMICAL APPARATUS AND PROCESS DISINFECTING, 

DEODORIZING, PRESERVING, OR STERILIZING 

423 Other 

technologies CHEMISTRY OF INORGANIC COMPOUNDS 

425 Other 

technologies 

PLASTIC ARTICLE OR EARTHENWARE SHAPING OR TREATING: 

APPARATUS 

426 Other 

technologies 

FOOD OR EDIBLE MATERIAL: PROCESSES, COMPOSITIONS, AND 

PRODUCTS 

427 Other 

technologies COATING PROCESSES 

428 Other 

technologies STOCK MATERIAL OR MISCELLANEOUS ARTICLES 

56 
429 Other 

technologies 

CHEMISTRY: ELECTRICAL CURRENT PRODUCING APPARATUS, 

PRODUCT, AND PROCESS 

430 Other 

technologies 

RADIATION IMAGERY CHEMISTRY: PROCESS, COMPOSITION, OR 

PRODUCT THEREOF 

431 Other 

technologies COMBUSTION 

432 Other 

technologies HEATING 

433 Other 

technologies DENTISTRY 

438 Other 

technologies SEMICONDUCTOR DEVICE MANUFACTURING: PROCESS 

439 Other 

technologies ELECTRICAL CONNECTORS 

440 Other 

technologies MARINE PROPULSION 

441 Other 

technologies BUOYS, RAFTS, AND AQUATIC DEVICES 

442 Other 

technologies 

FABRIC (WOVEN, KNITTED, OR NONWOVEN TEXTILE OR CLOTH, 

ETC.) 

445 Other 

technologies 

ELECTRIC LAMP OR SPACE DISCHARGE COMPONENT OR DEVICE 

MANUFACTURING 

57 
446 Other 

technologies AMUSEMENT DEVICES: TOYS 

449 Other 

technologies BEE CULTURE 

450 Other 

technologies FOUNDATION GARMENTS 

451 Other 

technologies ABRADING 

452 Other 

technologies BUTCHERING 

453 Other 

technologies COIN HANDLING 

454 Other 

technologies VENTILATION 

460 Other 

technologies CROP THRESHING OR SEPARATING 

462 Other 

technologies BOOKS, STRIPS, AND LEAVES FOR MANIFOLDING 

464 Other 

technologies 

ROTARY SHAFTS, GUDGEONS, HOUSINGS, AND FLEXIBLE 

COUPLINGS FOR ROTARY SHAFTS 

470 Other 

technologies 

THREADED, HEADED FASTENER, OR WASHER MAKING: PROCESS 

AND APPARATUS 

58 
472 Other 

technologies AMUSEMENT DEVICES 

473 Other 

technologies GAMES USING TANGIBLE PROJECTILE 

474 Other 

technologies 

ENDLESS BELT POWER TRANSMISSION SYSTEMS OR 

COMPONENTS 

475 Other 

technologies PLANETARY GEAR TRANSMISSION SYSTEMS OR COMPONENTS 

476 Other 

technologies FRICTION GEAR TRANSMISSION SYSTEMS OR COMPONENTS 

477 Other 

technologies 

INTERRELATED POWER DELIVERY CONTROLS, INCLUDING 

ENGINE CONTROL 

482 Other 

technologies EXERCISE DEVICES 

483 Other 

technologies TOOL CHANGING 

492 Other 

technologies ROLL OR ROLLER 

493 Other 

technologies 

MANUFACTURING CONTAINER OR TUBE FROM PAPER; OR 

OTHER MANUFACTURING FROM A SHEET OR WEB 

494 Other 

technologies IMPERFORATE BOWL: CENTRIFUGAL SEPARATORS 

59 
501 Other 

technologies COMPOSITIONS: CERAMIC 

502 Other 

technologies 

CATALYST, SOLID SORBENT, OR SUPPORT THEREFOR: PRODUCT 

OR PROCESS OF MAKING 

503 Other 

technologies 

RECORD RECEIVER HAVING PLURAL INTERACTIVE LEAVES OR A 

COLORLESS COLOR FORMER, METHOD OF USE, OR DEVELOPER 

THEREFOR 

504 Other 

technologies PLANT PROTECTING AND REGULATING COMPOSITIONS 

505 Other 

technologies 

SUPERCONDUCTOR TECHNOLOGY: APPARATUS, MATERIAL, 

PROCESS 

506 Other 

technologies 

COMBINATORIAL CHEMISTRY TECHNOLOGY: METHOD, LIBRARY, 

APPARATUS 

507 Other 

technologies EARTH BORING, WELL TREATING, AND OIL FIELD CHEMISTRY 

508 Other 

technologies 

SOLID ANTI-FRICTION DEVICES, MATERIALS THEREFOR, 

LUBRICANT OR SEPARANT COMPOSITIONS FOR MOVING SOLID 

SURFACES, AND MISCELLANEOUS MINERAL OIL COMPOSITIONS 

510 Other 

technologies 

CLEANING COMPOSITIONS FOR SOLID SURFACES, AUXILIARY 

COMPOSITIONS THEREFOR, OR PROCESSES OF PREPARING THE 

COMPOSITIONS 

512 Other 

technologies PERFUME COMPOSITIONS 

516 Other 

technologies 

COLLOID SYSTEMS AND WETTING AGENTS; SUBCOMBINATIONS 

THEREOF; PROCESSES OF 

60 
518 Other 

technologies 

CHEMISTRY: FISCHER-TROPSCH PROCESSES; OR PURIFICATION 

OR RECOVERY OF PRODUCTS THEREOF 

521 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

522 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

523 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

524 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

525 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

526 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

527 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

528 Other 

technologies 

SYNTHETIC RESINS OR NATURAL RUBBERS -- PART OF THE 

CLASS 520 SERIES 

530 Other 

technologies 

CHEMISTRY: NATURAL RESINS OR DERIVATIVES; PEPTIDES OR 

PROTEINS; LIGNINS OR REACTION PRODUCTS THEREOF 

534 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

61 
540 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

544 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

546 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

548 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

549 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

552 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

556 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

558 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

560 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

562 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

568 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

62 
 

570 Other 

technologies ORGANIC COMPOUNDS -- PART OF THE CLASS 532-570 SERIES 

585 Other 

technologies CHEMISTRY OF HYDROCARBON COMPOUNDS 

588 Other 

technologies 

HAZARDOUS OR TOXIC WASTE DESTRUCTION OR 

CONTAINMENT 

601 Other 

technologies SURGERY: KINESITHERAPY 

602 Other 

technologies SURGERY: SPLINT, BRACE, OR BANDAGE 

604 Other 

technologies SURGERY 

606 Other 

technologies SURGERY 

607 Other 

technologies SURGERY: LIGHT, THERMAL, AND ELECTRICAL APPLICATION 

623 Other 

technologies 

PROSTHESIS (I.E., ARTIFICIAL BODY MEMBERS), PARTS THEREOF, 

OR AIDS AND ACCESSORIES THEREFOR 

701 Other 

technologies 

DATA PROCESSING: VEHICLES, NAVIGATION, AND RELATIVE 

LOCATION 

702 Other 

technologies DATA PROCESSING: MEASURING, CALIBRATING, OR TESTING 

63 
703 Other 

technologies 

DATA PROCESSING: STRUCTURAL DESIGN, MODELING, 

SIMULATION, AND EMULATION 

706 Other 

technologies DATA PROCESSING: ARTIFICIAL INTELLIGENCE 

710 Other 

technologies 

ELECTRICAL COMPUTERS AND DIGITAL DATA PROCESSING 

SYSTEMS: INPUT/OUTPUT 

712 Other 

technologies 

ELECTRICAL COMPUTERS AND DIGITAL PROCESSING SYSTEMS: 

PROCESSING ARCHITECTURES AND INSTRUCTION PROCESSING 

(E.G., PROCESSORS) 

714 Other 

technologies 

ERROR DETECTION/CORRECTION AND FAULT 

DETECTION/RECOVERY 

716 Other 

technologies 

COMPUTER-AIDED DESIGN AND ANALYSIS OF CIRCUITS AND 

SEMICONDUCTOR MASKS 

717 Other 

technologies 

DATA PROCESSING: SOFTWARE DEVELOPMENT, INSTALLATION, 

AND MANAGEMENT 

718 Other 

technologies 

ELECTRICAL COMPUTERS AND DIGITAL PROCESSING SYSTEMS: 

VIRTUAL MACHINE TASK OR PROCESS MANAGEMENT OR TASK 

MANAGEMENT/CONTROL 

719 Other 

technologies 

ELECTRICAL COMPUTERS AND DIGITAL PROCESSING SYSTEMS: 

INTERPROGRAM COMMUNICATION OR INTERPROCESS 

COMMUNICATION (IPC) 

720 Other 

technologies DYNAMIC OPTICAL INFORMATION STORAGE OR RETRIEVAL 

850 Other 

technologies 

SCANNING-PROBE TECHNIQUES OR APPARATUS; APPLICATIONS 

OF SCANNING-PROBE TECHNIQUES, E.G., SCANNING PROBE 

MICROSCOPY [SPM] 

64