{"id":55430,"date":"2021-07-28T00:00:00","date_gmt":"2021-07-28T00:00:00","guid":{"rendered":"http:\/\/35.208.6.27\/xlscout_new\/natural-language-processing-driven-categorization\/"},"modified":"2024-10-16T09:55:25","modified_gmt":"2024-10-16T09:55:25","slug":"natural-language-processing-driven-categorization","status":"publish","type":"post","link":"https:\/\/xlscout.ai\/sv\/natural-language-processing-driven-categorization\/","title":{"rendered":"Natural Language Processing Driven Categorization\u00a0"},"content":{"rendered":"

AI-driven Patent Categories – Use of explainable AI<\/a><\/span> for Innovation \/ Ideation<\/a><\/strong><\/h2>\n

Patent and publication data is crucial for R&D teams. It gives tremendous insights on the market front, competitor trends and innovation, technology trends, and others. <\/span>However, patent data in its raw form is quite bulky to manage.<\/span><\/p>\n

In order to get the most out of patent data, it becomes essential to categorize, process, and visualize it. <\/span>Not only does patent categorization help in organizing patent data, it also assists in accelerating the analysis process. <\/span>Hence, patent categorization can aid businesses in gaining quick insights from the patent data. <\/span>However, categorizing such large ”patent data sets” manually can be a tedious and extensively time-consuming task.\u00a0<\/span>Categorizing patent data sets brings along a few challenges of its own.<\/span><\/p>\n

The main challenge is to decide the criteria for categorization.<\/p>\n

Important Parameters for Categorization<\/b><\/h2>\n

Classification based on the class codes is one of the more widely known methods. These class codes are globally recognized and standardized based on geography. <\/span>However, class code-based categorization misses out on other important parameters like semantics and concepts.<\/span><\/p>\n

The advancement of technology has paved the way for automation in patent categorization methodologies. AI is making its way through the intellectual property and innovation spaces. <\/span>There are many tools that use technologies like artificial intelligence, machine learning, and natural language processing (NLP) to expedite the patent categorization process.<\/span><\/p>\n

XLSCOUT offers modules that assist in innovation and R&D workflows, equipped with NLP and machine learning capabilities as an AI-based platform. The advanced BERT technology enables the tool to read through textual data, analyze it, and then categorize it into technology and sub-technology domains.<\/span><\/p>\n

XLSCOUT’s<\/a>\u00a0Techscaper<\/a> has capabilities that allow users to categorize large portfolios into system-generated or user-generated categories based on their requirements.<\/p>\n

The XLSCOUT Techscaper Provides 3 Ways to Get Landscape Analysis<\/strong><\/h2>\n

1. Instant Landscapes fully made by AI\u00a0<\/b><\/h3>\n

Within this segment, the machine applies intelligence and categorizes patents into auto-generated categories. This categorization is done based on the expert search logic that is set<\/span> on XLSCOUT<\/a><\/span>. The XLSCOUT corpus, which is machine-trained, assists the taxonomy with 3 billion-plus words.<\/span><\/p>\n

Takeaways:<\/b><\/h3>\n