{"id":55450,"date":"2022-07-07T00:00:00","date_gmt":"2022-07-07T00:00:00","guid":{"rendered":"http:\/\/35.208.6.27\/xlscout_new\/ai-nlp-based-engine-to-extract-deep-insights\/"},"modified":"2024-11-22T15:04:29","modified_gmt":"2024-11-22T15:04:29","slug":"ai-nlp-based-engine-to-extract-deep-insights","status":"publish","type":"post","link":"https:\/\/xlscout.ai\/ko\/ai-nlp-based-engine-to-extract-deep-insights\/","title":{"rendered":"Patent Landscape: Extract Deep Insights Using AI\/NLP Engine"},"content":{"rendered":"

Patent data is quite large, unstructured, and complex. You need cleaner, more structured data<\/a> to derive deep insights. Analysis is tough and time-consuming when dealing with large datasets. A tool that can quickly analyze massive amounts of data in a reasonable amount of time is required. An AI-based patent analytics tool can help with the analysis in this case.<\/p>\n

Structured Data Using AI-based Tools<\/strong><\/h2>\n

Most of these AI-based solutions employ various machine learning and natural language processing approaches during the data preprocessing stage, which involves preparing and cleaning data prior to analysis. This also demonstrates the significance of the phrase “Garbage In, Garbage Out,” which means that cleaner data leads to better analysis. It is difficult to clean up unstructured patent data without AI techniques. With AI tools that provide clean and structured data, it is now possible to run patent analytics on a large quantity of data. The AI\/NLP-based tools also have different visualization options to connect the dots between various data points and provide insights in a smooth manner.<\/p>\n

Visualization Using AI-based Tools<\/strong><\/h2>\n

With clean and structured data, AI-based tools provide users the flexibility of plotting different graphs between different data points to extract different insights for their various objectives. Visualizations such as 3D graphs and concept cloud maps, which would be impossible to create manually, are also provided by these tools.<\/p>\n

\"Technology
Technology breakdown Cloud Map (Source: XLSCOUT<\/a>)<\/figcaption><\/figure>\n

 <\/p>\n

\"Concept
Concept Cloud Map of user-defined clusters (Source: XLSCOUT<\/a>)<\/figcaption><\/figure>\n

AI helps in Extracting Insights<\/strong><\/h2>\n

In addition to visualization, classifying patents into various technology clusters<\/a> is critical for any landscape analysis. If done manually, the categorization of patents into these clusters requires a lot of time, effort, and expertise. The AI-based engines utilize various ML and NLP methods to automatically categorize the patents into different clusters. Some AI-based tools even offer users an option where users can define their own technology clusters for categorization.<\/p>\n

Our Methodology<\/strong><\/h2>\n

An innovative tool for explainable AI-based patent analytics has been created by XLSCOUT<\/a>. Here, users can extract various deep insights by leveraging our different technology and competitive dashboards. With just a few clicks, it creates a patent landscape report tailored to your needs. Our patent landscape modules have the capability to categorize enormous portfolios into system-generated or user-generated categories as per the user\u2019s requirements.<\/p>\n","protected":false},"excerpt":{"rendered":"

Patent data is quite large, unstructured, and complex. You need cleaner, more structured data to derive deep insights. Analysis is tough and time-consuming when dealing with large datasets. A tool that can quickly analyze massive amounts of data in a reasonable amount of time is required. An AI-based patent analytics tool can help with the…<\/p>\n","protected":false},"author":1,"featured_media":59622,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[189],"tags":[195],"class_list":["post-55450","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-techscaper"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/posts\/55450","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/comments?post=55450"}],"version-history":[{"count":0,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/posts\/55450\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/media\/59622"}],"wp:attachment":[{"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/media?parent=55450"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/categories?post=55450"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/tags?post=55450"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}