According to definition, White space analysis is a process of identifying the gaps or voids in technology to define a less crowded area in a patenting technology. White space analysis is otherwise known as white space opportunity mapping or grey area identification analysis.
The grey area can be classified into active whitespace and inactive whitespace, where the active whitespace defines the existence of opportunities in the technology and its possible advancement into the technology with minimal obstacles.
On the contrary, the inactive whitespace defines the existing voids in the technology but is bounded or restricted by the country’s rules and regulations that create an obstacle for the investment possibilities in the voided technology.
The White space analysis is used to create a way to identify competitors in the market, sales figures, voids, risk assessment, customer details, and other data. With the help of white space analysis, an entity can predict and decide whether to sell its products either horizontally or vertically.
Vertical selling is upselling which stands for selling a product in its upgraded version with a higher cost of selling and the term horizontal selling stands for cross-selling which describes the co-selling of products as supplements or compliments.
For instance, in vertical selling, the basic version and features of a product are updated to a new or higher level which improves the sales vertically, whereas in horizontal selling the products like laptop are accompanied by bags and stand, as a supplementary which widens the product sale.
The white space analysis is generally requested by an entity/business that intends to expand its market presence in a new domain. In short, the white space analysis is requested by a company that wants to expand and strengthen its IP portfolio, or by a new company to build an IP portfolio.
In one case, an entity intends to enter a new technology area or to find new alternative technology for their existing area of work. For instance, this white space analysis is helpful when an entity is working in the shoe-making process and that entity wants to enter the cosmetics domain.
In another case, this whitespace analysis is helpful when an entity is working in shoe-making technology and the entity looking for ways to improve the quality and effectiveness of the shoes by assessing a new technology or process for improving their existing shoe-making technology.
In general, the White space analysis is done as a part of patent landscape search and analysis. The process of identifying the grey area covers a few basic steps as same as that of the technology landscape search and analytics.
The first phase is the identification and segregation of a broad technology by major technologies, type, source, application, characteristic, method, and other similar categories. This is the same as that of the technology segmentation in the landscape.
This type of initial planning would provide an idea for the researcher to approach the process in an organized way to cover the entire broad domain. This can be done in both automatic and manual modes.
Several commercial patent databases offer specialized services to the companies looking for expanding their business to access the automatic landscape and whitespace technology segmentation and additional analysis. This type of analysis is customizable where the data can be adjusted according to the need of the company with a single click.
The second phase in patent whitespace is the retrieval of relevant patents, publications, non-patent literature, thesis and dissertations, commercial products, blogs, and other materials relating to the technology domain with its bibliographic details.
This can be done by performing the keyword-based search, patent classification-based search, inventor-based search, and the applicant or assignee-based search in both patent and non-patent database sources. The next step is data standardization. The data standardization includes
The third phase is the segmentation of the technologies as per the technological segmentation. Alternatively, the third phase is done by clustering the identified techniques from the citations under broad technologies.
The last phase in white space opportunity process is mapping of the major technologies with sub-technologies, and their attributes like methods, type of material, characteristics, and applications.
In addition, the non-patent literature is analyzed to identify the existing problems and their solutions and to address the future problems that can create hindrances during the process of research and development of the innovation.
The data can be provided in a matrix form with a heat map view and in a comparative way to show the challenges.
White space opportunity mapping is a process of mapping the technologies with their attributes like sub-technologies, characteristics, applications, and so on to find the innovation void in the domain. The focus of the mapping is based on the
The White spaces can be identified in several ways. They are
Generally, whitespace analysis is performed to anticipate the possible opportunities and threats in a technological domain. The predicted analysis may give comfort and reliability to the company to take the next investment step in their growth and venture with minimal risk.
In some cases, few companies do not want to proceed with the whitespace search and analysis, as they think that the predicted data is not reliable or valid which may lead to loss of investment, waste of effort, and time.
Initially, the technology of interest is segmented into parts based on the technical attributes and applications. Followed by retrieving the patents and non-patent literature and segmenting the retrieved citations based on the identified technologies or clustering the technologies available in the citations under broad technology.
For instance, if a broad technology is a microbial pesticide, the broad technology can be either segmented or the techniques can be clustered under microbial pesticide.
The segmentation describes that the microbial pesticide can be segmented type-wise, activity-wise, and application-wise.
The term “type wise” groups the type of microbes used as pesticides like bacteria, fungi, viruses, and other microbes.
Likewise, the term “activity wise” groups the activity of the microbes used for eradicating what type of organisms like weeds, insects, microbes, etc.
Similarly, the term “application wise” describes where the microbial pesticide is being used like clothing, house, wooden materials, agriculture, and so on.
Once the segmentation is over, the technology is mapped with sub-technologies or cross-technologies in a matrix to identify the less crowded areas.
The below matrix shows the crossing of technology versus sub technology with a heat map color indication.
The below matrix shows the crossing of technology versus application to identify the less crowded application.
The third analysis is to find the problems existing in a technological domain
Besides finding the technological voids and opportunities using a matrix, the comparison of technology with applicants, type of applicants, filing/priority dates, patent classification codes, and other attributes gives a clear vision of the innovation for investment purposes.
The Whitespace Analysis Business report contains various segments. They are
The grey area identification analysis can be done in both manual mode and an automatic mode and the report can be delivered in a pool of excel files having a list of relevant patents and non-patent literature manually segmented according to the white spaced domains and a PowerPoint report having research, recommendations, analytics, and insights.
On the other hand, several commercial patent databases give access to the entity to either automatically or manually analyze the patent literature to identify the basic insights required for white space analysis.
The whitespace analysis should be conducted in two scenarios. They are
This gap in the existing grey area can be analyzed by evaluating the problems that hinder the product quality and innovating appropriate solutions to overcome the hindrances.
The white space analysis is used by various levels of people in a company.
For instance, a voided area may have a few associated negative challenges in a space which may create some problems for the payers to enter the area. These negative challenges would help forecast the anticipated risk associated with new investments by the company.
The general cost for conducting whitespace analysis technology ranges between $3000 to $30000. The cost may go up to $50000 depending upon the innovation area and its attributes.
The whitespace analysis report can be customized and delivered in different formats, based on the client’s requirements.
The cost of the basic report having a pool of patents and non-patent literature without segmentation and analysis would range between $450 to $3000.
Likewise, the cost of the advanced report having a pool of patents and non-patent literature with technological segmentation, and analytics ranges between $1000-$30000.
As said earlier, the process of performing white space mind map analysis is similar to that of the patent landscapes to review the patent literature.
The tools that are helpful in this analysis process, are openly available patent databases and patent office databases like google patents, World Intellectual Property Organization – Patentscope, The lens, FreePatentsOnline, United States Patent and Trademark Office (USPTO), [email protected], Korean Institute of Patent Information (KIPRIS), Japan Patent Office (IPDL), Chinese Patent Office (SIPO) and many others.
There are few commercially available databases which are STN, Questel-Orbit, Thomson Innovation, PatBase, TotalPatent, and many others.
There are several tools available for retrieving commercial products and non-patent literature. These include search engines, journal websites such as Google Scholar, Science Direct, Nature, IEEE, and others, thesis and dissertations, and commercial websites.
TT consultants have developed an intellectual property tool called XLSCOUT which uses Artificial Intelligence and Machine Learning technologies for providing useful insights regarding the technologies.
Our tool provides insights into the pooling of patents and publications in a domain with a single click. The insights are,
With its intelligence powered by Artificial Intelligence and Machine Learning techniques, our one-click solution helps our team optimize time and effort by automatically providing the required details for the whitespace analysis in the early stages.
The analysis provided by the tool is customizable as per the needs and requirements of the client because of various intellectual property parameters.
A leading Multi-National Company client has approached us for a whitespace analysis in the domain of Tissue Engineering and Regeneration in Cosmetics.
Our team has used our unique ways of analyzing the domain and has provided them with a matrix having technology versus technology to show the information about the technology having less crowded patents/publications in the domain.
Our second insight was related to Technology Versus Applications to give an understanding of which application is having a less patenting activity.
Our third approach was to obtain perception from a non-patent literature search to obtain recent NPLs.
Our team has identified a few cross-domain-based non-patent literatures disclosing the use of a few compounds/stem cells in tissue engineering in the cardiovascular domain other than cosmetics.
Our additional efforts and insights have significantly helped the clients in a way for developing their inventions.
When it comes to performing invalidation search of any other IP search, a customer has various options. For example, one can get the search performed by an individual or an organization. Whereas an individual can only offer a limited expertise in a particular technology or IP field, an organization has various employees having abundant IP experience and diverse expertise in different technology domains.
Now when coming to organizations, again there are various choices available these days, and one should choose the best. TTC by all means is one of the best and renowned organizations in IP searching. And this did not happen overnight, TTC has an IP experience of almost two decades providing excellent and satisfying services to our consumers.
At TT Consultants, we apply intelligence to Intellectual Property (IP) globally. Our focus is to help clients solve their problems by providing useful strategies to them thus ensuring meticulous execution. We serve world’s largest companies, law firms, institutions, and several other IP fraternities. We help to monetize IP, defend IP litigation, lead innovation in industry and achieve most out of IP budgets.
We have various long-term clients who always choose TT as their go-to firm for diverse projects. We offer various services such as:
We also specialize in patent portfolios, patent rankings and technology landscapes. We further offer market research as well. We have specialized and expert teams for patent drafting, prosecution, office actions, etc.
TT Consultants is led by a highly experienced executive team committed to driving innovative solutions that deliver value to our clients. Our team comprises of 200+ experts in various technology areas such as electronics and communication, mechanical, pharma, biotech, life sciences, computer science, electrical and many more. Also, we have more than 25 PhDs with diverse expertise in various domains. We have 60+ experts having more than 10+ year of experience in IP.
In addition to that, we have IP experts related to us from US, UK, Taiwan, Japan and Canada having vast IP knowledge. Few of our team members have also served as USPTO patent examiners, thus we provide you with best of everything.
Also, we have our own in-house tools, XLPAT and XLSCOUT where the merging of human and machine learning gives best results. Humanizing intelligence from machine learning is primary foundation of analysis, insights, and informed decisions.
Proprietary algorithms, using Artificial Intelligence and Machine learning, led to evolution of a machine designed to mimic behaviour of an expert searcher. Various data sets include Patent data, Non-Patent Literature, Standard Essential Patents, Litigation data, Corporate data, Reassignment data, and Examination data.
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