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A two-year study sponsored
by ATP-EAO developed a new method of patent analysis called “hot-spots.” Hot
spots are technologies that are sparking a concentration
of inventive activity in the past few years. Hot spots can
be analyzed for their geographic characteristics, particularly
unexpected patterns of hot technological activity occurring
off the beaten path, away from easy sources of venture capital,
beyond known regions such as Silicon Valley, Route 128,
Austin, and Salt Lake City. Borrowing from a method used
in competitive intelligence studies to identify interesting
technologies, the modified hot-spots approach allows for
the identification of early-stage, high-risk technologies
like those ATP funds.
Five Major Findings
- ATP patents are twice as likely to be found among
the hot-spot related patents as in a similar sized sample
of the general population of patents.
The hot-spots
method identifies clusters of hot-spot patents (which
may be quite old) as well as a current set of patents
known as the next-generation patents. The next-generation
patents are a well-defined subset of recent patents
that is approximately 24% of the size of the general
population of recent patents. This subset contains 47%
of patents issued as a result of ATP sponsorship. This
was carried out for two time periods, and for both periods
the study found that roughly twice the number of ATP
patents was found in the next generation set as would
be expected. Moreover, this is a conservative estimate.
- and 3. ATP
patents have characteristics that can be identified
with real-time indicators, thus allowing a scoring
method to identify other potential early-stage,
high-risk technologies.
The vast majority
of newly issued patents are of little value, and
even those that do have value often are incremental
improvements on existing products or technologies.
The scoring method developed and validated in this
study has the ability to identify interesting emerging
technologies. All high-scoring clusters may not
be early-stage, high-risk technologies as defined
by ATP, but the majority would be described as being
interesting or emerging technologies as opposed
to incremental improvements, and a significant subset
can be described as being early-stage, high-risk
technologies.
Specifically,
technologies that are likely to be early-stage,
high-risk technologies can be identified by using
parameters found in patents, such as high science
linkage, and public sector participation, suggesting
the hot-spot/next-generation methodology provides
a means for identifying potentially early-stage
technologies of interest to ATP, other Federal programs,
and policymakers in a much more focused way than
in mining recent patents in general.
The high-scoring
clusters are in high-tech, interesting areas and were
not just incremental improvement inventions, as the
general population of U.S. patents tend to be. The top
assignees producing the high-scoring clusters tend to
be well known, well respected organizations such as
the University of California, MIT, General Electric,
IBM, etc. Universities provide much of the patents in
the high-scoring clusters, and moreover they provide
much of the foundation hot-spot patents which are then
built upon by companies in industries such as Biotechnology,
Pharmaceuticals, Semiconductors, and so on.
- From an
evaluation perspective, the ATP related patents
perform well above average in terms of the patent
indicators.
This
suggests that ATP-funded projects produce quality patents
that are used by other technologies and companies beyond
the ATP companies. In this way, the ATP related patents
represent a public good, such that ATP-funded projects are
likely to have a broad impact beyond the individual awardees.
- Current
ATP outreach and ATP funding is by and large reaching
the right metropolitan areas.
One
of the original goals of the project was to determine
metropolitan areas where ATP could do a better job of
outreach, by determining the metropolitan areas with
the most next-generation patents and comparing them
with the metropolitan areas that produced ATP applications.
The top 300 metropolitan areas were examined in both
cases and we found that the top metropolitan areas in
terms of high-tech patent production, with few exceptions,
tend to be the same areas in which ATP receives a number
of applications. This suggests that in the regions producing
high-tech patents, companies are aware of the ATP program
(see Table 1).
Table
1 - Rank and Percentage for Five Parameters by MSA (Sorted
by the Top Scoring Next-Gen Cluster Patent Rank)
| Metropolitan
Area |
ATP
Applications |
ATP
Awards |
Hot-Spot
Patents |
Next-Gen
(NG) Patents |
Top
S coring 100 NG Cluster Patents |
| San
Francisco-Oakland-San Jose, CA CMSA |
1
(9.7%) |
1
(10.7%) |
1
(17.6%) |
1
(17.5%) |
1
(14.9%) |
| Boston-Worcester-Lawrence-Lowell-Brockton, MA-NH NE CMA |
2
(7.2%) |
2
(7.9%) |
3
(5.5%) |
3
(4.8%) |
2
(8.6%) |
| New
York-Northern New Jersey-Long Island, NY-NJ -CT-PA
CMSA |
4
(6.2%) |
3
(6.0%) |
2
(9.5%) |
2
(7.8%) |
3
(7.4%) |
| Los
Angeles -Riverside-Orange County, CA CMSA |
5
(5.0%) |
6
(3.5%) |
4
(4.2%) |
4
(4.2%) |
4
(5.0%) |
| San
Diego, CA MSA |
7
(3.1%) |
10
(2.5%) |
6
(2.8%) |
9
(2.5%) |
5
(4.4%) |
| Washington-Baltimore,
DC -MD-VA-WV CMSA |
3
(6.3%) |
5
(4.4%) |
7
(2.7%) |
12
(2.1%) |
6
(3.9%) |
| Houston-Galveston-Brazoria, TX CMSA |
17
(1.5%) |
19
(1.2%) |
15
(1.7%) |
15
(1.7%) |
7
(3.3%) |
| Seattle-Tacoma-Bremerton,
WA CMSA |
19
(1.3%) |
22
(1.0%) |
14
(1.9%) |
13
(2.1%) |
8
(3.0%) |
| Minneapolis-St. Paul, MN-WI MSA |
12
(1.9%) |
9
(2.6%) |
8
(2.6%) |
6
(2.8%) |
9
(2.7%) |
| Philadelphia-Wilmington-Atlantic
City, PA-NJ -DE -MD CMSA |
8
(2.8%)) |
11
(2.5%) |
13
(2.1%) |
14
(1.9%) |
10
(2.6% |
| Raleigh-Durham-Chapel
Hill, NC MSA |
22
(1.1%) |
23
(0.9%) |
21
(1.2%) |
17
(1.6%) |
11
(2.2%) |
| Chicago-Gary-Kenosha,
IL-IN-WI CMSA |
9
(2.8%) |
8
(2.6%) |
5
(2.9%) |
7
(2.7%) |
12
(1.9%) |
| Detroit-Ann
Arbor-Flint, MI CMSA |
6
(3.7%) |
4
(5.1%) |
12
(2.1%) |
11
(2.2%) |
13
(1.9%) |
| Dallas-Fort Worth, TX CMSA |
18
(1.4%) |
16
(1.9%) |
11
(2.2%) |
10
(2.4%) |
14
(1.7%) |
| Denver-Boulder-Greeley,
CO CMSA |
11
(2.0%) |
14
(2.3%) |
19
(1.3%) |
22
(1.2%) |
15
(1.4%) |
| Austin-San
Marcos, TX MSA |
21
(1.1%) |
18
(1.3%) |
9
(2.4%) |
8
(2.7%) |
16
(1.2%) |
| Cincinnati-Hamilton,
OH-KY-IN CMSA |
28
(0.7%) |
29
(0.8%) |
25
(0.7%) |
24
(0.9%) |
17
(1.2%) |
| Phoenix-Mesa,
AZ MSA |
27
(0.7%) |
38
(0.5%) |
22
(1.1%) |
21
(1.2%) |
18
(1.1%) |
| New
Haven-Bridgeport-Stamford-Waterbury-Danbury, CT-NE
CMA |
20
(1.2%) |
17
(1.9%) |
16
(1.6%) |
18
(1.5%) |
19
(1.0%) |
| Rural
NY |
33
(0.6%) |
39
(0.5%) |
31
(0.5%) |
26
(0.7%) |
20
(1.0%) |
| Atlanta,
GA MSA |
13
(1.7%) |
21
(1.1%) |
20
(1.2%) |
20
(1.3%) |
21
(0.9%) |
| San
Antonio, TX MSA |
64
(0.2%) |
91
(0.2%) |
63
(0.2%) |
57
(0.3%) |
22
(0.9%) |
| Iowa
City, IA MSA |
177
(0.0%) |
999
(0.0%) |
134
(0.1%) |
118
(0.1%) |
23
(0.8%) |
| Boise
City, ID MSA |
114
(0.1%) |
999
(0.0%) |
10
(2.4%) |
5
(3.5%) |
24
(0.8%) |
| Portland-Salem,
OR -WA CMSA |
26
(0.7%) |
20
(1.2%) |
18
(1.4%) |
16
(1.6%) |
25
(0.8%) |
| Cleveland-Akron,
OH CMSA |
15
(1.6%) |
15
(2.0%) |
24
(0.9%) |
25
(0.8%) |
26
(0.8%) |
| Knoxville,
TN MSA |
36
(0.5%) |
55
(0.3%) |
58
(0.2%) |
84
(0.1%) |
27
(0.8%) |
| Columbus,
OH MSA |
25
(0.7%) |
25
(0.9%) |
39
(0.4%) |
47
(0.3%) |
28
(0.7%) |
| Rochester,
NY MSA |
29
(0.7%) |
26
(0.9%) |
17
(1.5%) |
19
(1.5%) |
29
(0.6%) |
| Madison,
WI MSA |
38
(0.5%) |
30
(0.8%) |
53
(0.3%) |
52
(0.3%) |
30
(0.6%) |
| Pittsburgh,
PA MSA |
14
(1.7%) |
13
(2.3%) |
23
(0.9%) |
30
(0.5%) |
31
(0.6%) |
| Milwaukee-Racine,
WI CMSA |
37
(0.5%) |
44
(0.5%) |
41
(0.4%) |
36
(0.5%) |
32
(0.6%) |
| Burlington,
VT-NE CMA |
176
(0.0%) |
999
(0.0%) |
35
(0.5%) |
23
(0.9%) |
33
(0.5%) |
| Hartford,
CT-NE CMA |
34
(0.6%) |
27
(0.9%) |
26
(0.6%) |
28
(0.6%) |
34
(0.5%) |
| Miami-Fort
Lauderdale, FL CMSA |
63
(0.2%) |
53
(0.4%) |
28
(0.6%) |
32
(0.5%) |
35
(0.5%) |
| Johnson
City-Kingsport-Bristol, TN-VA MSA |
207
(0.0%) |
94
(0.2%) |
121
(0.1%) |
94
(0.1%) |
36
(0.5%) |
| Orlando,
FL MSA |
39
(0.5%) |
34
(0.6%) |
74
(0.2%) |
44
(0.4%) |
37
(0.5%) |
| Lexington,
KY MSA |
126
(0.1%) |
91
(0.2%) |
108
(0.1%) |
59
(0.2%) |
38
(0.5%) |
| Melbourne-Titusville-Palm
Bay, FL MSA |
125
(0.1%) |
113
(0.1%) |
71
(0.2%) |
68
(0.2%) |
39
(0.5%) |
| Omaha,
NE-IA MSA |
106
(0.1%) |
87
(0.2%) |
146
(0.1%) |
136
(0.1%) |
40
(0.5%) |
| Jacksonville,
FL MSA |
100
(0.1%) |
999
(0.0%) |
151
(0.0%) |
147
(0.1%) |
41
(0.5%) |
| Salt
Lake City-Ogden, UT MSA |
23
(0.9%) |
28
(0.8%) |
32
(0.5%) |
29
(0.5%) |
42
(0.4%) |
| Providence-Warwick-Pawtucket,
RI-NE CMA |
47
(0.4%) |
41
(0.5%) |
55
(0.3%) |
50
(0.3%) |
43
(0.4%) |
| Albuquerque,
NM MSA |
32
(0.6%) |
33
(0.6%) |
43
(0.3%) |
58
(0.2%) |
44
(0.4%) |
| Oklahoma
City, OK MSA |
124
(0.1%) |
999
(0.0%) |
93
(0.1%) |
91
(0.1%) |
45
(0.4%) |
| Nashville,
TN MSA |
95
(0.2%) |
999
(0.0%) |
64
(0.2%) |
106
(0.1%) |
46
(0.4%) |
| Charleston,
WV MSA |
175
(0.0%) |
133
(0.1%) |
163
(0.0%) |
142
(0.1%) |
47
(0.4%) |
| Fort
Myers-Cape Coral, FL MSA |
999
(0.0%) |
999
(0.0%) |
172
(0.0%) |
194
(0.0%) |
48
(0.4%) |
| West
Palm Beach-Boca Raton, FL MSA |
79
(0.2%) |
70
(0.2%) |
34
(0.5%) |
45
(0.4%) |
49
(0.3%) |
| Santa
Barbara-Santa Maria-Lompoc, CA MSA |
31
(0.6%) |
43
(0.5%) |
56
(0.3%) |
62
(0.2%) |
50
(0.3%) |
Factsheet
1.C7 (March 2005 by Connie Chang)
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