{"id":55441,"date":"2021-11-02T00:00:00","date_gmt":"2021-11-02T00:00:00","guid":{"rendered":"http:\/\/35.208.6.27\/xlscout_new\/searchable-patent-problems-dataset-now-available-in-xlscout\/"},"modified":"2024-10-16T09:58:52","modified_gmt":"2024-10-16T09:58:52","slug":"searchable-patent-problems-dataset-now-available-in-xlscout","status":"publish","type":"post","link":"https:\/\/xlscout.ai\/ko\/searchable-patent-problems-dataset-now-available-in-xlscout\/","title":{"rendered":"Searchable Patent “Problems” Dataset now available in XLSCOUT"},"content":{"rendered":"

XLSCOUT<\/strong><\/a> has launched Patent Problems Search, which is a new way to overcome 101\/102 rejections. We have explained the details in this guide. All the human power in an in-house Patent Department OR Prosecution Attorneys can’t seem to overcome 101\/102 rejections. Using Natural Language Processing and Big Data XLSCOUT ‘s <\/strong><\/a>problems dataset might have an effective answer. Give it a try.<\/p>\n

XLSCOUT has launched a unique Problems Search- a searchable dataset of problems (problems that patents are solving) in XLSCOUT This searchable dataset uses NLP and can be leveraged to find and visualize and overcome examiner objections that problems have previously been solved\/resolved in an alternative way.<\/p>\n

The insights companies discover might be key to\u00a0overcoming examiner rejections for good.<\/p>\n

Read on to learn about the\u00a0XLSCOUT problem search feature.<\/strong><\/a><\/p>\n

XLSCOUT uses explainable AI technology to help\u00a0innovators solve\u00a0their\u00a0patent search and research problems.<\/p>\n

What Is XLSCOUT Problem Search?<\/strong><\/h2>\n

How long would it take\u00a0the average patent attorney to pour through a Patent set and extract Problems from Patents over time?<\/p>\n

Is that the best use of time when limited to a keyword search? It might help them to find a few notable competitors, but it doesn’t get to the root of most rejections.<\/p>\n

The XLSCOUT problem search feature allows you to conduct a problem search in patents.<\/p>\n

Users\u00a0aren’t limited to competitors, either. The feature also allows\u00a0them to search for problems that prior art is solving, too.<\/p>\n

The Role of Explainable AI and NLP in Competitor\/ Landscape Analysis<\/p>\n

The problem search owes its accuracy to XLSCOUT’s accurate and reliable\u00a0explainable AI technology<\/strong><\/a>. In addition to powering the search through the world’s largest dataset, AI powers the dashboards, reports, and technology database to make the results of\u00a0any search visual and interactive.<\/p>\n

Besides eliminating redundancy, departments can use this data to determine exactly how and why competitors are succeeding. Additionally, it can illuminate the new niches those departments can be targeting themselves.\u00a0This will not only improve your existing patents but may lead to new, bespoke innovation.<\/p>\n

For any innovator seeking the answers for\u00a0how to\u00a0overcome 101\/102 rejections<\/strong><\/a>, the key is insight. All XLSCOUT reports are data-driven, reliable, and specific to save you time and put you a step ahead of competitors.<\/p>\n

Innovate Now with Problems Search in Patents<\/strong><\/h2>\n

IP departments, entrepreneurs, and companies want to spend more time innovating and less time remediating. It’s time to start overcoming examiner objections.<\/p>\n

Problems search in XLSCOUT provides the insight necessary to locate redundancy, find new niches, and succeed. By harnessing the power of explainable AI and NLP, XLCSCOUT patents search will provide\u00a0innovators with the data they need to see their patents approved before their competitors.<\/p>\n","protected":false},"excerpt":{"rendered":"

XLSCOUT has launched Patent Problems Search, which is a new way to overcome 101\/102 rejections. We have explained the details in this guide. All the human power in an in-house Patent Department OR Prosecution Attorneys can’t seem to overcome 101\/102 rejections. Using Natural Language Processing and Big Data XLSCOUT ‘s problems dataset might have an…<\/p>\n","protected":false},"author":1,"featured_media":59876,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[189],"tags":[190],"class_list":["post-55441","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-general"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/posts\/55441","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=55441"}],"version-history":[{"count":0,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/posts\/55441\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/media\/59876"}],"wp:attachment":[{"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/media?parent=55441"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/categories?post=55441"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xlscout.ai\/ko\/wp-json\/wp\/v2\/tags?post=55441"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}