Companies’ Intellectual Property Departments are doing fantastic work to protect their company’s research work, maintain existing assets, and make various IP-related decisions based on their capabilities and knowledge. However, with the improvement of AI technologies, dependence on machines to make specific decisions within Intellectual Property Departments has succeeded in causing disruption and has the potential to overpower the most promising IP search methodologies.
The tech adaptive Intellectual Property Departments are devoting the majority of their time to making decisions based on the results or insights generated by machine. AI technologies are playing a critical role in automating the standard IP search workflow. Moreover, these technologies are helping IP departments reduce the time they spend on various IP searches and patent searches.
1. Automation in Novelty Checking
Patentability searches are conducted in-house or outsourced by Intellectual Property Departments on inventions submitted by R&D departments. Using NLP, machine learning, and semantic analysis, AI technologies are revolutionizing patentability searches. Experts first submit inventions to AI engines in order to receive novelty reports and visualizations in order to determine their potential next steps.
Novelty Reports: AI provides evidence of overlap with prior art as well as novel elements.
Visualization: Competitor protection in the relevant technology. Improving Patent Quality.
Intellectual Property Departments are now taking a more structured approach to filing IP; it is more focused and with explicit knowledge of where completion is at, and ONLY a focused set of patent applications are indeed being taken forward-the culling process is real-time and quicker using Artificial Intelligence.
XLSCOUT’s Methodology
XLSCOUT put the use of reinforcement learning to its AI-based Novelty Checker tool to get quality prior art search reports in just 10 minutes. 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 a novelty search to help you ensure that your innovation is unique. By selecting a few relevant and non-relevant results, users can apply reinforcement learning to the result set. The system takes the user’s feedback and then learns from it. Then it re-ranks the results by bringing the quality results to the top and sending the noise to the bottom.
Without reinforcement learning, users go through hundreds of results manually. By applying reinforcement learning, users can skip going through the non-relevant results. Users can apply reinforcement multiple times to a result set according to their different requirements/ criteria. They can then view the Top-10 or Top-20 results for each criterion to perform an analysis for idea validation.
Users can quickly generate an automated novelty and patentability search report by selecting these Top-10 or 20 results. Search reports from Novelty Checker include a good list of results. In addition, key features of the invention are mapped with relevant text to facilitate quick decision making.
2. Decisions to abandon, renew, or out-license
Competitor product tracking is a continuing process that necessitates repeated searches at regular intervals. AI algorithms are convenient because they replace time-consuming manual searches with an automated alert system.
XLSCOUT provides you with a competitive dashboard where you can compare the portfolios of different competitors and extract different insights like their filing trends, top problems they are trying to solve, their technology investments, their collaborations, their core patents, etc. With just a few clicks, it creates a patent landscape report tailored to your needs.
3. Identifying Important Patents in a Portfolio
Finding important patents in a portfolio is a time-consuming task where automation can help by evaluating predefined parameters. In addition, by comparing market and competitor information with patent data, AI technologies can mimic the behavior of expert searchers.
4. Automation in Invalidity Searching
Automation of Invalidation Searches saves approximately 60% of the time. Using NLP and machine learning technologies, AI can understand the meaning of text data and match similarities between different documents.
Invalidator+ by XLSCOUT is an artificial intelligence (AI)-enabled app that allows users to quickly access prior art by focusing on specific competitors’ patent claims. Users can supervise the machine by selecting claims of interest and technical variations/keywords after entering the subject patent number. Along with the analysis and report, the tool generates ranked prior art references.
5. Systematic dissemination of information using sophisticated tools
Implementing AI-based solutions can address the issue of keeping global offices and different departments in sync with technology advancements and upgrades. Some companies’ IP departments are collaborating with AI vendors to set up a Single Repository Alert system for focused alerts and syncing alerts across global offices.
For more information, visit xlscout.ai to learn how AI-based intellectual property intelligence tools can help you make better and informed decisions!
