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Patent searching and analysis have proven their utility in taking strategic business decisions, new acquisitions, exploring new geographies, and others. With the increasing patent data at a pace experienced never before, patent searching and analysis with precision has become the need of the hour. There are no set methods for patent searching and insight mining, however here are some of the most common ways to approach:
Classification based patent search
Searching patents by classifications are the most common and efficient patent searching method. Classifications are standardized and globally acknowledged across global patent offices, classification-based search comes across as one of the most reliable and widely acknowledged methods. This search technique especially commands the patent spaces from other geographies where precise translations are required which keyword-based searches usually lack.
Predominantly, there are four classification models:
- The US classification system (USPTO)
- European Classification system (ECLA)
- WIPO
- Japan’s F Terms.
While many members of the European Classification use IP as well.
Besides, the CPC classification is another popularly acknowledged classification system primarily used in the US and Europe. The CPC is a combination of ECLA and USPTO models and is also a superset of IPC classifications.
Each of these models has their own unique features and advantages and provide organized ways to conduct a patent search.
Keyword-based searches
Keyword-based searching has been popularised across geographies because by far it is the easiest way of searching. However, with the level of complexity involved, the inclusion of synonyms, semantics, and inter-operable search terms has become standard practice.
Clustering patents while searching
Patent clustering can impact patent searching and analysis in a positive way. The clustering of patents can be done either on the basis of classification codes or concepts or technology. Additional filters like Assignee and Geography-based clustering also help in focussing the search query to specifics.
Clustering however is a complex process as it involves the categorization of data into multiple overlapping clusters and manual clustering is often tedious and time taking. Recently, NLP-equipped AI-based tools are assisting the searchers and analysts in segregating the patent data and clustering it down for further analysis.
XLSCOUT, one of the emerging tools in the domain, offers modules equipped with NLP technology BERT, that enables seamless clustering of patents by creating automated and human-assisted (Hybrid) taxonomies.
Categorization through tool-generated (automated) taxonomy
This taxonomy or clustering is done through advanced NLP concepts tagged and trained on a large patent data set. The training set provides a window for tools to create citations that act as reference points for further classification.
Machine assisted categorization on manual taxonomy
This allows users to create manual categories and then allows the tool to cluster the data set based on the user-created taxonomy. The tool applies BERT technology to analyze patents, identify concepts and then hence cluster them according to the provided categories.
Using the right Boolean expressions
Boolean operators provide an edge to further optimize the search query and direct it to the specifics users are interested in. Along with the common Boolean operators like AND, OR and NOT, operators like NEAR, ADJ, BTWN are commonly used by expert searchers.
Boolean operators when used in sync with classification based search strings and keyword-based search strings often yield the desired results.
Add an extra layer of search
The automation surely helps in adding another padding to the search process but it is not limited to it. These tools can facilitate Prior Art searches because of higher precision, the ability to segment the problem statement into smaller buckets, and considerably lesser turnaround time.
Even with machine advancement and technological supremacy, Artificial Intelligence or Machine Learning is not a replacement for Human Intelligence. The technology can only aid in analyzing the bulk of data and break it down into smaller chunks for further analysis.
XLSCOUT supports modules and tools that can enable users to get a kick start and conduct a first pass AI-assisted analysis.
Using Trend analysis and data visualization:
Textual data, even post-analysis, involves a considerable amount of time and that’s where graphical interpretation, data visualization, and tabular analysis make data easier to interpret and present.
There are intelligent tools that can do graphical analysis on large patent datasets and derive insights that have direct business implications. XLSCOUT is one such intelligent platform that provides users with graphical analysis in the form of interactive charts which can be customized as per the requirements.
XLSCOUT offers multiple AI-assisted solutions that enable your patent searches and other IP workflows more efficient. These can be easily integrated with existing systems to further streamline the processes
For more information on how XLSCOUT can help in strategizing decision making through its advanced algorithms, reach out to us at: contact@xlscout.ai
