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IPVectors Mission
Our mission is to empower companies to innovate with greater efficiency and confidence while safeguarding their inventions. Our AI driven analytics delivers the most comprehensive and precise patent analytics and comparisons. With these actionable insights, inventors and engineers as well as IP practioners can efficiently identify and navigate white space and innovation opportunities – and target the most promising acquisition and licensing candidates.
What We Do
IPV Patent Analysis generates detailed patent comparisons and reports using AI, large language models (LLMs) and proprietary scoring algorithms. This enables contextual searches and similarity scoring across large volumes of patents and produces easy to digest summaries enabling the user to dive deeper into the patents and claims that really matter.
What Makes Us Different?
IPV analysis is unique in the field of patent analytics since it generates embeddings for the compared patents on demand, (as each related CPC collection is examined). Other industry leading vendors that are leveraging AI depend instead on a heavy overhead model that requires them to repeatedly download complete updates of the entire patent office collection, (typically with the need for weekly updates) in order to generate embeddings for entire collections so they can respond to specific requests. The requests they receive relate only a tiny portion of the entire collection (millions of patents) that they have to support by generating transformer embeddings.
Embeddings Explained
IPVectors.com uses LLMs, Scoring, and Vector Embedding to Revolutionize Patent Search and Analysis
The application of large language models (LLMs) combined with vector embeddings and scoring mechanisms represents a significant leap forward from traditional keyword and Boolean-based patent search methods. Historically, patent searches were limited by the rigid structure of keyword matching or Boolean logic, often missing critical pieces of prior art due to linguistic variation or ambiguity in descriptions. This was problematic because it led to either irrelevant results or missed prior art, making the patent search process inefficient and incomplete.
Embeddings, a key advancement in this new approach, are mathematical representations of words and phrases that capture not only their meaning but also their context. In simpler terms, embeddings convert words or entire texts into numerical vectors that are positioned in a high-dimensional space. Words or phrases with similar meanings will be closer together in this space, even if the actual terms used are different. This allows LLMs to recognize that two patents might describe the same invention in different ways, enabling a much more accurate and comprehensive prior art search.
This contextual understanding is crucial in patent law, where the nuances of language can define the difference between novelty and infringement. By leveraging embeddings and advanced scoring, LLMs can produce a much more precise and relevant set of results, helping both law firms and inventors navigate the increasingly complex patent landscape. Finding the most pertinent prior art early can help inventors avoid wasting time and resources on patents that may not stand up to scrutiny or might infringe on existing patents.
Advanced Analytics and the Future of IP Strategy
For law firms, adopting these advanced tools is becoming essential. Managing clients' patent portfolios requires not only filing and protecting intellectual property but also strategically guiding them through potential IP licensing or acquisition opportunities. The ability to more accurately identify white space, potential partners, or even competitors' vulnerabilities is invaluable. Additionally, improving the identification of prior art helps firms reduce the risk of infringement lawsuits, saving their clients from costly legal battles.
What’s even more important is not just the identification of potential patent opportunities through AI-driven analytics, but also access to the comprehensive details that IP law firms need to provide sound advice to their clients. This includes filing dates, the status of the patent, ownership information, assignees, and the law firms involved. Having access to this data in a structured, accessible format can be the difference between making an informed decision about pursuing a licensing deal, entering negotiations for acquisition, or identifying potential partners for joint ventures.
