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Leverage the power of Large Language Model Vectara to automate and streamline the patent litigation workflow by automating text generation in your patent applications for prior art search, patent drafting, patent analysis, and subject matter categorization. The platform aims to save time, reduce costs, and improve the overall effectiveness of patent litigation processes built by integrating cutting-edge technologies such as Vectara, a large language model, with patent-specific datasets obtained from Google Patents. Improve accuracy by reducing errors caused by manual entry. Minor inconsistencies can lead to costly litigation- minimizing trivial errors, increasing technical accuracy and focusing on the patent application quality reducing poor patent decisions in the absence of patent analytics. Avoid costly mistakes down the line with filing patents. Cost-effective reviews of their patent applications and analyze them in minutes. Time-savings and improved accuracy translate to direct savings and increasing accessibility of patents in the process. Generate text based on patent claims with RAG and grounded generation to eliminate hallucinations with a text corpus from Google Patents. Creating a system for generating text based on patent claims using RAG and grounded generation, coupled with a text corpus from Google Patents, can significantly improve the quality and reliability of generated content. By incorporating grounding techniques, such as fact-checking against a reputable corpus, you can mitigate hallucinations and ensure that the generated text is based on factual information. The retrieval component can use the structured corpus data to find relevant information related to a specific patent claim. Implement fact-checking mechanisms to validate the accuracy of the generated text. If the generated content contradicts the corpus data, the system should flag or modify it.
