The document presents a methodology for determining the technological route between pioneering inventions and incremental improvements using patent data. The methodology involves: (1) performing an initial patent search, (2) analyzing patent trends over time to calculate an "intellectual property density", (3) fitting the density data to logistic curves to identify major technological phases, and (4) using text clustering to identify new areas of innovation. As a case study, the methodology is applied to analyze the evolution of textile loom weft insertion technologies from 1932 to 2008. Three major technological phases were identified from the logistic curve fitting, each characterized by midpoint year and other metrics.

1. Technological Route between Pioneerism and Improvement
Roberto Nani
1
, Daniele Regazzoni
2
1
Scinte s.n.c., Ranica, Italy
2
University of Bergamo, Dalmine, Italy
Abstract
This paper presents a systematic approach to determine the technological route between discovery,
pioneering, radical creation and qualitative or quantitative improvement as the two extremities of a line of
evolution regarding a certain inventive theme. The technological route is the result of a procedure based on
a wide use of a patent search engine capable of Browsing Codes according to International Patent
Classifications, and Clustering Texts to examine search results using linguistic technologies, and a tool
able to analyse time data series to disclose their eventual logistic behaviour.
Said procedure consists of four main steps: (i) determination of the initial search algorithm based on the
characteristics of reference subject, patent or group of patents; (ii) definition and calculus of Intellectual
Property Density (IPD) on the base of bibliometric results of search; (iii) IPD fitting into logistic curves and
(iv) definition of a quick and semi-automated way to gather some inputs for further product innovation.
The procedure still requires a minimum skill in patent searching and handling and search may have to be
iterated some times before getting the desired outcome. By the way there is no need to read patents text
and the time spent is comparable to a traditional search (i.e. according to EPO guidelines for search).
The results obtained consist in fostering both forecasting activities by identifying quantitatively main
evolution phase and problem solving activities by giving a dynamic view of the state of the art so that future
problems may be taken into account in today solutions.
One out of the several case studies already tested with this approach is presented with the aim of
describing in detail each step performed.
Keywords
Patent Data Base (PDB), International Patent Classification (IPC), Intellectual Property Density IPD, logistic
S-curves, emerging technologies
1 INTRODUCTION
Among TRIZ users the importance given to patent
resources is far behind the mere protection of R&D
results. Patents represent a starting point for new
inventions and a huge resource for collecting information
on the way contradictions have been solved and in which
different field such solutions may be adopted. Moreover
the worldwide patent database contains information about
the technology evolution that can be extracted so that the
level of maturity of a product or process can be evaluated.
The connection among patent resources and problem
solving activities can be improved in order to provide the
innovators with meaningful data in short times whenever a
problem occurs. The amount of time needed and the
unreliability of traditional patent search results cause
problem solvers to under exploit patents derived data. To
overcome this issue several attempts to make it better
and more automated can be found in literature [3,6,7].
The aim of this paper is to differentiate from detailed and
high resources time consuming approaches such as [3,7].
The novelty proposed consists in a simple and efficient
experience-based approach to perform innovation driven
patent investigations.
2 GOAL AND METHODOLOGY
The goal of this paper is to present an organized set of
steps to clearly identify the patent state of the art of a
certain product or technology, so that further research
activities such as technological forecasting could be
performed. The main reasons why this work has been
carried out are the increasing demand of technology
assessment based on patent information, and the growing
industrial awareness of intellectual property as an asset to
foster innovation.
The methodology is made up of four main steps as
reported in Figure 1. The starting point is as general as
possible and could simply be a request of information
about the state of the art of a specific technology, a device
or a function in a specific context. Once defined focus and
boundaries of the matter to investigate on, the first step is
performed. By browsing or searching in the patent
classification the classes characterizing the matter are
identified. Then a usual patent investigation is performed
by crossing keywords and classes search. The procedure
can be iterated several times to refine keywords on the
base of the results obtained.
Once the patent set is satisfying, in step 2 some
statistical-bibliometric analyses are performed. In
particular patents distribution and patents classification
over time are plotted. Afterwards, Intellectual Property
Density (IPD) has been introduced by the authors as the
ratio between the cumulated number of patents filed till a
specific year and the count of 4 digits classes involved till
the same year. According to the authors, IPD describes
quite easily the general trend characterizing relevant
innovations evolution. The first patents filed when a new
invention appears disclose highly innovative devices or
technology that may have a wide spectrum of application
and potential fields of use [8]. As the patents leading the
way are a few and they can be found in several different
patent classes, the IPD of an emerging technology is quite
low. During technology evolution the number of patents
increases and they get more and more focused on sub-
systems or details that are often classified in a small
number of classes. As a consequence IPD in maturity
stage is much higher. There is not scientific evidence that
the curve plotting IPD over time has recurrent trend, by
the way the experimental case studies accomplished so
far have always produced qualitatively S-shaped curves.

2. Figure 1: methodology steps
The third step of the paradigm consists in fitting the IPD
data in logistic curves [1], [4], [5]. Data can be adequately
filtered, if needed, to exclude peaks due to known reasons
or to cut out the first or last figures. To do so we used a
software package for analyzing logistic behaviour
developed and shared by the University of Rockefeller
(NY) called Loglet Lab. Loglet Lab allows to discern and
analyze S-shaped curve or a succession of many S-
shaped curves even if overlapped in time. If the patent set
obtained in step 2 and its relative IPD trend results to be
logistic, one or more curves can be found with minimal
residuals. Each quantitative S-curve describes a
generation of device or technology, giving precious data
on mean and saturation times.
The results of step 3 can be exploited for at least two
different kinds of activities. If the goal of the research is to
understand the trends of evolution, further observations
can be done to support strategic R&D activities planning.
Future events can be foreseen by identifying the super-
system conditions that influenced certain evolutions path
despite of others, reasoning on the resources that will run
out first or applying structured tool to perform a complete
technological forecasting [8].
The second way the results achieved can be exploited
concerns problem solving by deriving solutions from
different domains. Step four describes a singular way to
perform this task. Starting form an S-curve describing IPD
density behaviour we may presume that the mean year is
the one in which patents filed are mostly concentrated on
the specific device or technology characterizing the curve.
This can be explain by the fact that, when the technology
reaches its maximum growth, the interferences of the
previous and following technologies is the lowest. Thus
when the technologies are strictly substituting one another
(non concurrent in time) the interference is negligible. This
approach is used to dramatically decrease the number of
patent to work on, confidently not compromising the final
result.
At this point a basic clustering algorithm is applied to the
set of patents so that most occurring words are found and
grouped in order to minimize cluster interactions.
Depending on the number of patents and on their
homogeneity different number of clusters can be found.
Filtering clusters containing general terms is quite easy to
identify one or two set of words characterizing the novelty.
Then a new patent search is done using such words as
keywords without imposing any class constraint.
The result gathered is considered in terms of patent class
to identify commonalities with the starting matter, in terms
of problem to address or solutions found.
The fourth step is quite complex but completely
automated and its definition is derived by practical
application. Actually, it is not time consuming and the
advice achieved mostly lead to valuable results.
The following part of the paper is dedicated to the
application of the steps described so far in order to give
detailed guidelines to perform innovation oriented patent
investigation.
3 METHODOLOGY APPLICATION
The following paragraphs report the application of the four
step methodology to a real case study concerning textile
looms. In particular the focus is put on the weft insertion
technology of a weaving machine, which has the overall
function of gripping a fibre and moving it along a specific
path.
3.1 Step 1: preliminary patent search
To search for patents describing the state of the art of
weft insertion technology the following Boolean algorithm
has been used with a patent database [10]:
((gripper*) <in> (TITLE,ABSTRACT,CLAIMS) )
AND ((weft* OR thread* OR filament* <in> (TITLE,
ABSTRACT, CLAIMS))) (1)
returning about 6.500 documents of European, American
and International patents and applications:
Collections searched: European (Applications - Full text),
European (Granted - Full text), US (Granted - Full text),
WIPO PCT Publications (Full text), US (Applications -
Full text)
6,458 matches found of 11,531,757 patents searched
distributed according to International Patent Classification
(IPC) in the following classes:
IPC-R Code- 4 digit Items %
Bar
Chart
D03D D — Textiles; Paper;
Weaving; Woven
776 8.1 %
B65H B — Performing Operations;
Transporting; Conveying
614 6.4 %
B65B B — Performing Operations;
Transporting; Conveying
448 4.6 %
B25J B — Performing Operations;
Transporting; Hand Tools
399 4.1 %
B41F B — Performing Operations;
Transporting; Printing
312 3.2 %
A61B A — Human Necessities;
Medical or Veterinary Science; H
283 2.9 %
B65G B — Performing Operations;
Transporting; Conveying
262 2.7 %
E21B E — Fixed Constructions;
Earth or Rock Drilling; Mining
245 2.5 %
B25B B — Performing Operations;
Transporting; Hand Tools
233 2.4 %
B29C B — Performing Operations;
Transporting; Working of PLA
212 2.2 %

3. 10 rows shown 3784
(Below cut-off) 5,769 60.4 ...
Table 1: IPC distribution of patents of preliminary search
The final documental collection of the “filamentary
manipulation (gripping)” is obtained by the following
Boolean query that crosses the function “to grip” with the
most representative classes:
(((d03d OR b65h) <in> IC ) AND
((gripp*) <in> (TITLE,ABSTRACT,CLAIMS))) (2)
Collections searched: European (Applications - Full text),
European (Granted - Full text), US (Granted - Full text),
WIPO PCT Publications (Full text), US (Applications - Full
text)
8,700 matches found of 11,531,757 patents searched
3.2 Step 2: patents analysis and IPD determination
According to the results obtained with the algorithm (2)
the “filamentary manipulation (gripping)” starts its
technological path on 1932 with a “gripper operating
mechanism” (Figure 2). From 1932 to 2008 the knowledge
of “filamentary manipulation (gripping)” characterizes filed
patents and applications capturing electric, mechanic,
chemic resources from the technological branches.
Figure 2: first patent considered concerning filamentary
manipulation (gripping)
From the first patent to the last completed year data are
collected and plotted in order to highlight:
- number of patents filed per year (Figure 3);
- number of 4 digits IPC classes in which patents
are classified per year (Figure 4);
- Cumulative number of patents filed per year
(Figure 5);
- Total count of classes in which patents have
been classified per year (Figure 6).
0
50
100
150
200
250
300
350
400
450
Filed
Year
1958
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
Figure 3: Number of patents per year
0
10
20
30
40
50
60
70
80
90
100
Filed
Year
1958
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
Figure 4: Number of involved IPC classes per year
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
Filed
Year
1958
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
Figure 5: Cumulative number of patents filed per year
0
50
100
150
200
250
300
350
Filed
Year
1930 1970 1975 1980 1985 1990 1995 2000 2005 2008
Figure 6: Total count of classes per year

4. The statistical-bibliometric analysis is performed to obtain
the data needed to calculate the Intellectual Property
Density (IPD) number as the ratio between the cumulative
number of patents and the classes involved (i.e. the ratio
among the figures plotted in Figure 5 and Figure 6). The
curve describing IPD behaviour over time is shown in
Figure 7.
0
5
10
15
20
25
30
Filed
Year
1930
1965
1970
1975
1980
1985
1990
1995
2000
2005
2008
Figure 7: Intellectual Property Density IPD
3.3 Step 3: logistic curves fitting
This step is the most important as it provides the empirical
evidence that IPD trend is a logistic trend. By using Loglet
Lab software we found out that the distribution of Figure 7
is actually fitting with minimal residuals (3% maximum) to
a set of three S-curves. Figure 8-10 show respectively the
S-shaped curves, the bell curves and the Fisher-Pry
transform curves.
Figure 8: S-shaped curves fitting IPD data
Figure 9: Bell-shaped curves fitting IPD data
Figure 10: Fisher-Pry transform curves fitting IPD data
From the fitting of the curves three main technologies
concerning “filamentary manipulation (gripping)” emerged.
Each curve is characterized by the following data:
• Technology I: midpoint = 1972; growth time = 14.4;
saturation = 6.7;
• Technology II: midpoint = 1987; growth time = 11.7;
saturation = 9.8;
• Technology III: midpoint = 1999; growth time = 17.3;
saturation = 12.4.
The technologies defined in this way have to be
interpreted according to breakthrough technological
changes regarding, for examples, new materials, new
chemical-physical discoveries/principles or due to socio-
political events, new standards.
On the next paragraph a clustering algorithm is applied to
applications and patents of said saturation points:
(((d03d OR b65h) <in> IC ) AND
((gripp*) <in> (TITLE,ABSTRACT,CLAIMS))) (2)
Filed Year Items %
Bar
Chart
1999 305 3.5 %
1987 320 3.7 %
1972 112 1.3 %
Table 2: number of patents
3.4 Step 4: new IPC classes of interest
In the next paragraphs 3.4.1, 3.4.2 and 3.4.3 a clustering
algorithm is applied to patents and applications of specific
saturation points: 1972, 1987 and 1999. Analyzing the text
of patents and applications, terms are grouped in order to
obtain the minimum number of cluster having almost no
cross connections. The result can be automatically shown
by a graph. Each cluster is composed by a number of
terms, describing both devices and actions quite
homogeneous for meaning and field. A quick scan of the
terms is enough to associate TRIZ Inventive Principles to
each cluster. By the way there no need to go through all
the clusters if in the working context a specific goal is
defined.

5. 3.4.1 technology I: midpoint = 1972; growth time =
14.4; saturation = 6.7
A clustering overview of patents and applications filed on
1972 considers specific devices and actions constituting
the Boolean algorithm (4):
Cluster Overview 5 Clusters
For work file: 1972 (112 items )
Cluster Descriptive words
1 comprise, loom, roller, provide, apparatus,
mean, move, device, control, include
2 sheet, mechanism, gripper, mount, press,
transfer, conveyor, include, movement, printing
3 form, engage, surface, end, material, drive,
provide, apparatus, lower, length
4 mean, member, parallel, relative, grip, pair,
include, release, pass, groove
5 release, side, arrange, adjacent, movement,
locate, operation, hold, first, time
Table 3: clustering overview of collection 1972
Figure 11: 5 clusters graph of collection 1972
((release AND groove AND arrange AND movement) <in>
(TITLE,ABSTRACT,CLAIMS)) (4)
Collections searched: European (Applications - Full
text), European (Granted - Full text), US (Granted -
Full text), WIPO PCT Publications (Full text), US
(Applications - Full text)
6,400 matches found of 11,374,720 patents
searched
The Boolean algorithm (4) produces a collection of about
6.400 documents, the IPC resource being the
technological branch (IPC):
- F16L 37/00 Couplings of the quick-acting type
;
- A61M 5/00 Devices for bringing media into the
body in a subcutaneous, intra-vascular or
intramuscular way; Accessories therefor, e.g.
filling or cleaning devices, arm rests .
3.4.2 technology II: midpoint = 1987; growth time =
11.7; saturation = 9.8
A clustering overview of patents and applications filed on
1987 considers specific devices and actions constituting
the Boolean algorithm (5):
Cluster Overview 10 Clusters
For work file: 1987 (320 items )
Cluster Descriptive words
1 mount, support, include, horizontal, comprise,
use, adjacent, rotation, engage, recess
2 apply, stack, apparatus, grip, relate, say,
method, improve, feeding, position
3 roll, web, direction, material, contact, winding,
device, determine, include, part
4 sheet, apart, grip, spread, example, transport,
location, first, move, apparatus
5 arrange, fold, gripper, printed product,
apparatus, advance, first, extend, needle,
invention
6 clamp, weft, thread, side, element, move, shed,
weft thread, loom, end
7 mount, provide, shaft, include, cam, position,
drive, dispose, introduce, end
8 support, surface, rotation, point, outer, tube,
extend, end, form, press
9 move, object, stack, conveyor, deliver, include,
grip, release, vacuum, carry
10 process, transfer, arm, transport, perform,
mean, position, control, path, sheet
Table 4: clustering overview of collection 1987

6. Figure 12: 5 clusters graph of collection 1987
((support AND rotation AND tube AND extend AND press)
<in> (TITLE,ABSTRACT,CLAIMS)) (5)
Collections searched: European (Applications - Full
text), European (Granted - Full text), US (Granted -
Full text), WIPO PCT Publications (Full text), US
(Applications - Full text)
1,210 matches found of 11,374,720 patents
searched
The Boolean algorithm (5) produces a collection of about
1.200 documents, the IPC resource being the
technological branch (IPC):
- B29C SHAPING OR JOINING OF PLASTICS;
SHAPING OF SUBSTANCES IN A PLASTIC STATE, IN
GENERAL; AFTER- TREATMENT OF THE SHAPED
PRODUCTS, e.g. REPAIRING.
3.4.3 technology III: midpoint = 1999; growth time =
17.3; saturation = 12.4
A clustering overview of patents and applications filed on
1999 considers specific devices and actions constituting
the Boolean algorithm (6):
Cluster Overview 5 Clusters
For work file: 1999 (305 items )
Cluster Descriptive words
1 sheet, device, form, grip, machine, method,
station, gripper, position, comprise
2 invention, relate, say, gripper, comprise, define,
provide, include, section, guide
3 mean, apparatus, control, arrange, transfer,
provide, convey, product, form, first
4 include, move, position, first, grip, stack,
engage, apparatus, rotation, hold
5 end, support, portion, reel, rotatably, comprise,
mount, shaft, arrange, provide
Table 5: clustering overview of collection 1999
Figure 13: 5 clusters graph of collection 1999
((include AND move AND position AND engage AND
rotation AND hold) <in> (TITLE,ABSTRACT,CLAIMS))
(6)
Collections searched: European (Applications - Full
text), European (Granted - Full text), US (Granted -
Full text), WIPO PCT Publications (Full text), US
(Applications - Full text)
21,483 matches found of 11,374,720 patents searched
The Boolean algorithm (6) produces a collection of about
21000 documents, the IPC resource being the
technological branch (IPC):
- G11B 17/00 Guiding record carriers not
specifically of filamentary or web form, or of
supports therefore.

7. 4 CONCLUSIONS
This paper shows an experience-based way to perform a
patent search capable of gathering not only the static
state of the art of a specific technological domain, but
providing quantitative data to rely on to perform problem
solving as well as technological forecasting. The
methodology is rooted in a simple but unconventional use
of patent search engine and of a software package for
logistic curve analysis available for free. The most
relevant result achieved within the experience behind this
paper is that the ratio among number of patents and their
distribution on IPC classes, called Intellectual Property
Density, behaves in a logistic way. This assumption is
based on a number of case studies differing for field of
applications and maturity of technology in which the IPD
trend has always shown such characteristic.
After the evolution steps of a device or a technology have
been defined and each of them is associated to a
quantitative S-shaped curve, both problem solving and
technological forecasting can be performed with relevant
benefits. Actually the maturity level of present state of the
art systems is assessed and even maturity and decay
period can be foreseen and taken into account to search
for solutions exploiting resources that won’t run out in the
near future or already thought to overcome problems that
still have to appear.
Concerning the specific application reported in this paper
the emerging technological collections defines new
resources that are practically unknown from prior art
analysis by Boolean algorithm (2), as shown on table 6:
• F16L 37/00 Couplings of the quick-acting type;
• A61M 5/00 Devices for bringing media into the body
in a subcutaneous, intra-vascular or intramuscular
way; Accessories therefor, e.g. filling or cleaning
devices, arm rests ;
• B29C SHAPING OR JOINING OF PLASTICS;
SHAPING OF SUBSTANCES IN A PLASTIC STATE,
IN GENERAL; AFTER- TREATMENT OF THE
SHAPED PRODUCTS, e.g. REPAIRING;
• G11B 17/00 Guiding record carriers not specifically
of filamentary or web form, or of supports therefore.
IPC-R Code- 4 digit Items % Bar Chart
B65H B — Performing
Operations; Transporting;
Conveying
6375
45.0
%
D03D D — Textiles; Paper;
Weaving; Woven
1140 8.0 %
B41F B — Performing
Operations; Transporting;
Printing
853 6.0 %
….. ….. …. …..
B29C B — Performing
Operations; Transporting;
Working of PLA
184 1.3 %
…. …. …. …
G11B G — Physics;
Information Storage;
Information Storage B
63 0.4 %
…. ….. …. …
F16L F — Mechanical 21 0.1 %
Engineering; Lighting;
Heating
… …. …. ….
A61M A — Human
Necessities; Medical or
Veterinary Science; H
8 0.0 %
Table 6: confront of collocation of new IPC resources
respect to main IPC classes of prior art Boolean algorithm
(2)
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CONTACT
Roberto Nani
Scinte s.n.c.
24020, Ranica (BG), Italy
E-mail: info@scinte.com
Phone: +39 (035) 513683
FAX: +39 (035) 513683
Daniele Regazzoni
University of Bergamo,
24044, Dalmine (BG), Italy
E-mail: daniele.regazzoni@unibg.it
Phone: +39 (035) 2052353
FAX: +39 (035) 2052077