Important API Change
The new version of the API requires an API key, or all of your requests will be blocked. See the API Changes page.
The following is a brief foray into patent citation networks. The analysis is done on 3 patents that describe patent citation analysis (PCA) themselves.
The first step is to download the relevant data from the PatentsView API. We can use the CPC code of Y10S707/933 to identify the patents that relate to PCA.
library(patentsview)
library(dplyr)
library(visNetwork)
library(magrittr)
library(stringr)
library(knitr)
# Write a query to pull patents assigned to the CPC code of "Y10S707/933"
query <- qry_funs$contains(cpc_current.cpc_group_id = "Y10S707/933")
pv_out <- search_pv(query = query, fields = c("patent_id", "patent_title"))
patent_ids <- pv_out$data$patents$patent_id
pat_lst <- unnest_pv_data(pv_out$data, pk = "patent_id")
# We have to go against the patent_citiation endpoint now, these fields
# are no longer available from the patent endpoint
citing_query <- qry_funs$eq(patent_id = patent_ids)
cited_query <- qry_funs$eq(citation_patent_id = patent_ids)
# Create a list of fields to pull from the API. Note that, with a recent chanage
# to the R package, these aren't the only fields we'll receive back from the API.
# This is due to the way that the API's paging works now. There is more about this
# in the [Result Set Paging](result-set-paging.html) vignette.
fields <- c(
"patent_id",
"citation_patent_id"
)
# Post a request to the API
res <- search_pv(citing_query,
fields = fields, all_pages = TRUE,
endpoint = "patent/us_patent_citation", method = "POST", size = 1000
)
res2 <- search_pv(cited_query,
fields = fields, all_pages = TRUE,
endpoint = "patent/us_patent_citation", method = "POST", size = 1000
)
# Unnest the data found in the three lists of columns
# (citation_sequence being a field we didn't request)
res_lst <- unnest_pv_data(res$data, pk = "patent_id")
res_lst
#> List of 1
#> $ us_patent_citations:'data.frame': 1066 obs. of 3 variables:
#> ..$ patent_id : chr [1:1066] "10095778" ...
#> ..$ citation_sequence : int [1:1066] 0 1 ...
#> ..$ citation_patent_id: chr [1:1066] "4991087" ...
res_lst2 <- unnest_pv_data(res2$data, pk = "patent_id")
res_lst2
#> List of 1
#> $ us_patent_citations:'data.frame': 1056 obs. of 3 variables:
#> ..$ patent_id : chr [1:1056] "10007946" ...
#> ..$ citation_sequence : int [1:1056] 53 13 ...
#> ..$ citation_patent_id: chr [1:1056] "7716226" ...Count the distinct patent_id and cited_patent_id
There are only 11 PCA patents. These patents cite 1066 patents and
are cited by 1056 patents. Let’s visualize the citations among the PCA
patents. We’ll create our visualization using the
visNetwork package, which requires us to create a data
frame of nodes and a data frame of edges.
pat_title <- function(title, number) {
temp_title <- str_wrap(title)
i <- gsub("\\n", "<br>", temp_title)
paste0('<a href="https://patents.google.com/patent/US', number, '">', i, "</a>")
}
edges <-
res_lst$us_patent_citations %>%
semi_join(x = ., y = ., by = c("citation_patent_id" = "patent_id")) %>%
select(-citation_sequence) %>% # discard citation_sequence that we don't need here
set_colnames(c("from", "to"))
nodes <-
pat_lst$patents %>%
mutate(
id = patent_id,
label = patent_id,
title = pat_title(patent_title, patent_id)
)
visNetwork(
nodes = nodes, edges = edges, height = "400px", width = "100%",
main = "Citations among patent citation analysis (PCA) patents"
) %>%
visEdges(arrows = list(to = list(enabled = TRUE))) %>%
visIgraphLayout()
#> Error in loadNamespace(name): there is no package called 'webshot'It looks like several of the patents cite patent number 6,499,026, perhaps indicating that this patent contains technology that is foundational to the field. However, when we hover over the nodes we see that several of the patents have the same title. Clicking on the titles brings us to their full text on Google Patents, which confirms that many of these PCA patents belong to the same patent family.1 Let’s choose one of the patents in each family to act as the family’s representative. This will reduce the size of the subsequent network, while hopefully retaining its overall structure.
p3 <- c("7797336", "9075849", "6499026")
res_lst2 <- lapply(res_lst, function(x) x[x$patent_id %in% p3, ])With only 3 patents, it will probably be possible to visualize how these patents’ cited and citing patents are all related to one another. Let’s create a list of these “relevant patents” (i.e., the 3 patents plus all of their cited and citing patents)2, and then get a list of all of their cited patents (i.e., the patents that they cite). This list of cited patents will allow us to measure how similar the relevant patents are to one another.
rel_pats <-
res_lst$us_patent_citations %>%
rbind(setNames(res_lst$us_patent_citations, names(.))) %>%
select(-patent_id) %>%
rename(patent_id = citation_patent_id) %>%
bind_rows(data.frame(patent_id = p3)) %>%
distinct() %>%
filter(!is.na(patent_id))
# Look up which patents the relevant patents cite. We need to use the
# patent_citation endpoint now.
rel_pats_res <- search_pv(
query = list(patent_id = rel_pats$patent_id),
fields = c("citation_patent_id", "patent_id"),
all_pages = TRUE, size = 1000, method = "POST", endpoint = "patent/us_patent_citation"
)
rel_pats_lst <- unnest_pv_data(rel_pats_res$data, "patent_id")Now we know which patents the 4855 relevant patents cite. This allows us to measure the similarity between the 4855 patents by seeing how many cited references they share in common (a method known as bibliographic coupling).
cited_pats <-
rel_pats_lst$us_patent_citations %>%
filter(!is.na(citation_patent_id))
full_network <-
cited_pats %>%
do({
.$ind <-
group_by(., patent_id) %>%
group_indices()
group_by(., patent_id) %>%
mutate(sqrt_num_cited = sqrt(n()))
}) %>%
inner_join(x = ., y = ., by = "citation_patent_id") %>%
filter(ind.x > ind.y) %>%
group_by(patent_id.x, patent_id.y) %>%
mutate(cosine_sim = n() / (sqrt_num_cited.x * sqrt_num_cited.y)) %>%
ungroup() %>%
select(matches("patent_id\\.|cosine_sim")) %>%
distinct()
kable(head(full_network))| patent_id.x | patent_id.y | cosine_sim |
|---|---|---|
| 4555775 | 4533910 | 0.1443376 |
| 4736308 | 4555775 | 0.1543033 |
| 4772882 | 4555775 | 0.1178511 |
| 4772882 | 4736308 | 0.2182179 |
| 4812834 | 4533910 | 0.2236068 |
| 4812834 | 4772882 | 0.0912871 |
full_network contains the similarity score
(cosine_sim) for all patent pairs that share at least one
cited reference in common. This means that it probably contains a lot of
patent pairs that have only one or two cited references in common, and
thus aren’t all that similar. Let’s try to identify a natural level of
cosine_sim to filter on so that our subsequent network is
not too hairy.
hist(
full_network$cosine_sim,
main = "Similarity scores between patents relevant to PCA",
xlab = "Cosine similarity", ylab = "Number of patent pairs"
)
There appears to be a smallish group of patent pairs that are very
similar to one another (cosine_sim > 0.8), which makes
it tempting to choose 0.8 as a cutoff point. However, patent pairs that
have reference lists that are this similar to each other are probably
just patents in the same patent family. Let’s choose 0.1 as a cutoff
point instead, as there doesn’t appear to be too many pairs above this
point.3
edges <-
full_network %>%
filter(cosine_sim >= .1) %>%
rename(from = patent_id.x, to = patent_id.y, value = cosine_sim) %>%
mutate(title = paste("Cosine similarity =", as.character(round(value, 3))))
nodes <-
rel_pats_lst$us_patent_citations %>%
distinct(patent_id) %>%
rename(id = patent_id) %>%
mutate(
# the 3 patents of interest will be represented as blue nodes, all others
# will be yellow
color = ifelse(id %in% p3, "#97C2FC", "#DDCC77"),
label = id,
title = pat_title(id, id) # we don't get patent_title now (formerly first argument)
)
visNetwork(
nodes = nodes, edges = edges, height = "700px", width = "100%",
main = "Network of patents relevant to PCA"
) %>%
visEdges(color = list(color = "#343434")) %>%
visOptions(highlightNearest = list(enabled = TRUE, degree = 1)) %>%
visIgraphLayout()
#> Error in loadNamespace(name): there is no package called 'webshot'