How to conduct better patent landscape study
Few examples are, inclusion of false positives, wrong reassignment information, wrong legal status, faulty family family build, No. unique id for family, etc.
Solution: Next time you start analysing the data, pay attention to families which are unusually large, assignees that are not known to be active in the researched area (say Nokia in Pharma), kind codes of Asian countries (esp JP,CN), do random checks on the details provided by the database
Pitfall 2 : Using wrong bibliographic field for answering right question
Patent data can provide direction towards answering business and technical questions. But are you using the right metric to do that? Also, are you providing incomplete answers by only looking at a certain field only? These are the most common mistakes that I have observed in landscapes available publicly.
Solution: Understand and analyze the problem. The best way is to break a question into smaller questions and attack it by analyzing various fields. A top quality solution is to have a direct answer complemented by data to validate. You should know when to use latest publication data vs. latest priority date vs. earliest priority date. Patent is a legal instrument. To be a skilled analyst, you need not be a lawyer but should have very good understanding on patent law and prosecution process.
Pitfall 3 : Overly process centric approach
Do you have set rules/templates/macros to do your landscape report? Is yes, then you may be called a killer of creativity. Pick up books on business research and IP research, a lot of authors and experts suggest that patent analysis is a science (so called patinformatics) but at the same time it is an art.
Solution: I do not say we should not have tools to make the analysis process efficient. However, while doing any analysis one should think about the problem, or the research question and be creative in presenting and conducting research. Remember, you are also an artist!
Pitfall 4 : Complex category structure to address simple objectives
In many occasions, patents need to be categorized into logical categories to answer specific question or to understand parts of a technology/company. It is recommended to follow the famous MECE principle to do this. However, in many instances the risk related to taxonomy/bucketing/categorization schedule is to go either very deep or very shallow. This may lead to complete failure, in-correct insights, wasted effort, and over complexity of technical details.
Solution: Spend enough time in preparing a usable taxonomy, discuss with the end users (if possible), test the category on regular basis. If there is a need, it is not bad to have overlapping categories and non compliance to MECE principle. The technical subject may be too complex to have exclusive category or you may not even need very complex category. The taxonomy should be flexible enough to include exceptions and odd items. While creating a taxonomy schedule, KNOW WHEN TO STOP & BE FLEXIBLE!
Pitfall 5 : Great analysis but bad communication.
I think this is one of the most common pitfall. You may need to report your findings/research output/insights to your client/researcher/marketing team/ BD team. The analyst or researcher may not always gets an opportunity to present his/her findings to the end client. However, this may cause great loss both the end users and the research team. Sometime the bits of information that analysts do not feel worthy to report are really useful to the end user. Also, timely feedback from the end users help the research team to plan their research accordingly for the future. Role of communication is not overstated in all the management text books, communication is also an important part of the entire landscape research exercise.
Solution: It is highly recommended to have an open discussion between the landscape researcher and the end user. In my view, not all great technically skilled person are good communicators. Hence, they should either identify one in the team or up skill themselves to communicate the ideas clearly. The big message is "GO BEYOND DATA DUMP"!
Happy Researching!
Please note that these are my personal opinions not my employer’s.
In
my previous posts (Myth Series one, two), I tried to list some of the myths
related to the use of patent landscape studies. In this post, I will
explore common pitfalls while executing a landscape study and extracting
the highest value from the analysis. This post can be useful to
analysts and managers with responsibilities to conduct and/or commission
patent landscape studies. Some of these points are also valid for business, marketing, and general research.
Pitfall 1 : Data import/export is not always correct
As
analysts, we rely on third party databases and the provided data
exports. We rely on the result set appearing after uploading the
publication number. But do we go back and check if they are correct?Few examples are, inclusion of false positives, wrong reassignment information, wrong legal status, faulty family family build, No. unique id for family, etc.
Solution: Next time you start analysing the data, pay attention to families which are unusually large, assignees that are not known to be active in the researched area (say Nokia in Pharma), kind codes of Asian countries (esp JP,CN), do random checks on the details provided by the database
Pitfall 2 : Using wrong bibliographic field for answering right question
Patent data can provide direction towards answering business and technical questions. But are you using the right metric to do that? Also, are you providing incomplete answers by only looking at a certain field only? These are the most common mistakes that I have observed in landscapes available publicly.
Solution: Understand and analyze the problem. The best way is to break a question into smaller questions and attack it by analyzing various fields. A top quality solution is to have a direct answer complemented by data to validate. You should know when to use latest publication data vs. latest priority date vs. earliest priority date. Patent is a legal instrument. To be a skilled analyst, you need not be a lawyer but should have very good understanding on patent law and prosecution process.
Pitfall 3 : Overly process centric approach
Do you have set rules/templates/macros to do your landscape report? Is yes, then you may be called a killer of creativity. Pick up books on business research and IP research, a lot of authors and experts suggest that patent analysis is a science (so called patinformatics) but at the same time it is an art.
Solution: I do not say we should not have tools to make the analysis process efficient. However, while doing any analysis one should think about the problem, or the research question and be creative in presenting and conducting research. Remember, you are also an artist!
Pitfall 4 : Complex category structure to address simple objectives
In many occasions, patents need to be categorized into logical categories to answer specific question or to understand parts of a technology/company. It is recommended to follow the famous MECE principle to do this. However, in many instances the risk related to taxonomy/bucketing/categorization schedule is to go either very deep or very shallow. This may lead to complete failure, in-correct insights, wasted effort, and over complexity of technical details.
Solution: Spend enough time in preparing a usable taxonomy, discuss with the end users (if possible), test the category on regular basis. If there is a need, it is not bad to have overlapping categories and non compliance to MECE principle. The technical subject may be too complex to have exclusive category or you may not even need very complex category. The taxonomy should be flexible enough to include exceptions and odd items. While creating a taxonomy schedule, KNOW WHEN TO STOP & BE FLEXIBLE!
Pitfall 5 : Great analysis but bad communication.
I think this is one of the most common pitfall. You may need to report your findings/research output/insights to your client/researcher/marketing team/ BD team. The analyst or researcher may not always gets an opportunity to present his/her findings to the end client. However, this may cause great loss both the end users and the research team. Sometime the bits of information that analysts do not feel worthy to report are really useful to the end user. Also, timely feedback from the end users help the research team to plan their research accordingly for the future. Role of communication is not overstated in all the management text books, communication is also an important part of the entire landscape research exercise.
Solution: It is highly recommended to have an open discussion between the landscape researcher and the end user. In my view, not all great technically skilled person are good communicators. Hence, they should either identify one in the team or up skill themselves to communicate the ideas clearly. The big message is "GO BEYOND DATA DUMP"!
I
hope you will find this useful and next time you will watch your
research steps to avoid these pitfalls. Comments & suggestions
are invited.
Happy Researching!
Please note that these are my personal opinions not my employer’s.
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