{"id":55661,"date":"2023-02-07T00:00:00","date_gmt":"2023-02-07T00:00:00","guid":{"rendered":"http:\/\/35.208.6.27\/xlscout_new\/how-can-xlscout-save-inventors-from-failing-at-the-patent-office\/"},"modified":"2024-10-29T07:11:10","modified_gmt":"2024-10-29T07:11:10","slug":"how-can-xlscout-save-inventors-from-failing-at-the-patent-office","status":"publish","type":"post","link":"https:\/\/xlscout.ai\/nl\/how-can-xlscout-save-inventors-from-failing-at-the-patent-office\/","title":{"rendered":"How can XLSCOUT Save Inventors from Failing at The Patent Office?"},"content":{"rendered":"

According to statistics on patent rejection, the likelihood of failing at the patent office is much higher than the likelihood of receiving the patent. <\/span>You have an 88.6%<\/a> chance of not getting a patent for your invention.<\/span>\u00a0<\/span><\/p>\n

The harsh reality is that a lot of inventors have their patent applications rejected. Perhaps because their creation is not novel or is considered obvious.<\/span>\u00a0<\/span><\/p>\n

Reasons for Rejecting the Patent Application<\/b><\/span>\u00a0<\/span><\/h2>\n

The high rejection rate may be due, in part, to inventors’ ignorance of the prior art. Prior art<\/a> is simply an invention that is similar to yours. Either as a commercially available product or as a patent or non-patent document concept. This is supported by the statistics on patent rejections, where USC 35 103<\/a> is frequently rejected.<\/span>\u00a0<\/span><\/p>\n

102 Type Rejection<\/span><\/b>\u00a0<\/span><\/h3>\n

When a patent application’s claims are invalidated by exact prior art, the examiner issues rejections of type 102.<\/span>\u00a0<\/span><\/p>\n

103 Type Rejection<\/span><\/b>\u00a0<\/span><\/h3>\n

When combining two or more references to demonstrate that an invention disclosed in a patent application is obvious, the examiner assigns 103 type rejection.<\/span>\u00a0<\/span><\/p>\n

The time it takes to obtain a patent and the cost of filing a patent both increase as the number of rejections rises. This deters small businesses with limited resources, particularly startups and independent inventors. And to top it off, even if a patent is approved, there is little chance that it will be financially viable.<\/span>\u00a0<\/span><\/p>\n

Existing prior art that demonstrates the invention is either “not new” or “obvious” is one of the main reasons for failing at the patent office. Therefore, it’s time to better prepare inventors for success at the patent office.<\/span>\u00a0<\/span><\/p>\n

A Potential Solution<\/span><\/b>\u00a0<\/span><\/h2>\n

An inventor would therefore be in a better position to decide whether to proceed or not if there was an intelligent tool that could inform them of existing alternatives or references that might cause them to consider their invention to be obvious or not novel. Additionally, they might discuss this information with their lawyer during a consultation. This could result in a claim amendment before submitting an application or another similar strategy.<\/span>\u00a0<\/span><\/p>\n

Such a clever tool might be able to remove the cloud of doubt hanging over the patent application process. Additionally, it might make it easier for informed, data-supported patent filing strategies, which could reduce the cost, duration, and number of rejections associated with patent prosecution.<\/span>\u00a0<\/span><\/p>\n

How XLSCOUT can help?<\/span><\/b><\/h2>\n

The\u00a0Novelty Checker<\/a>\u00a0from\u00a0XLSCOUT<\/a>\u00a0makes prior art searches easier for inventors. The tool searches for similar inventions to yours to determine whether a patent is feasible.<\/span>\u00a0<\/span><\/p>\n

A step-by-step guide to conducting an AI-based patent search with Novelty Checker can be accessed by clicking here<\/a>.<\/p>\n

XLSCOUT put the use of reinforcement learning to its AI-based Novelty Checker (patent searching tool) to get\u00a0<\/span>quality patent research reports in just 5 minutes<\/span><\/a>. The Novelty Checker uses reinforcement learning to filter the noise from the prior art by pulling up the relevant results on top of the list. To be precise, it assists in conducting\u00a0<\/span>patentability search<\/span><\/a>\u00a0to help you ensure that your innovation is unique. By selecting a few relevant and non-relevant results, users can apply them to the result set. The system takes the user\u2019s feedback and then learns from it. It uses\u00a0<\/span>conceptual searching<\/span><\/a>\u00a0and re-ranks the results by bringing the quality results to the top and sending the noise to the bottom.\u00a0\u00a0\u00a0<\/span>\u00a0<\/span><\/p>\n

Without reinforcement learning, users go through hundreds of results manually. By applying this process, users can skip going through the non-relevant results. Reinforcement can also be applied multiple times to a result set according to users\u2019 different requirements\/criteria. Users can then view the Top-10 or Top-20 results for each criterion to perform a prior-art analysis for idea validation.\u00a0<\/span>Users can quickly generate an automated novelty report by selecting these Top-10 or 20 results.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

According to statistics on patent rejection, the likelihood of failing at the patent office is much higher than the likelihood of receiving the patent. You have an 88.6% chance of not getting a patent for your invention.\u00a0 The harsh reality is that a lot of inventors have their patent applications rejected. Perhaps because their creation…<\/p>\n","protected":false},"author":1,"featured_media":58707,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[189],"tags":[230],"class_list":["post-55661","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-novelty-checker-llm"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/posts\/55661","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/comments?post=55661"}],"version-history":[{"count":0,"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/posts\/55661\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/media\/58707"}],"wp:attachment":[{"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/media?parent=55661"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/categories?post=55661"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/xlscout.ai\/nl\/wp-json\/wp\/v2\/tags?post=55661"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}