SBIR Phase I: From Search to Research with Fast Patent-document Correlations

SBIR第一阶段项目试图解决创新者的一个基本问题：我的想法已经获得专利了吗？ 这项自助服务将使小型企业能够在10分钟内根据想法中包含的概念对超过1000万份专利文件进行综合研究。这种全面、实时、低成本的服务目前还不适用于小企业家。 目前的搜索工具不将结果合成为执行摘要，不允许将整个文件作为搜索输入输入，并且不执行实时概念/相关性计算。 这项创新将使发明者能够提交整个文档（想法）作为搜索查询;然后，利用网络数学和人工智能算法，这项服务将实时合成搜索结果，总结哪些专利文档与自然语言处理的想法最相关。 这种针对小企业家的服务将使他们能够初步确定他们的想法的新奇，并为他们提供关于专利文件中使用的自然语言与他们的想法进行比较的在线教育。 这种元创新将客观地确定任何拟议技术的知识产权价值，使小企业创新者能够，并促进美国创新发展的加速。潜在语义分析（LSA）的发展使算法开发能够提取潜在语义。（或隐藏的）语义结构，解决了文本匹配搜索无法解决的两个重要的词义模糊问题：一词多义（具有多种含义的单个术语;即，（1）“以物易物，以物易物，以物易物”。汽车和汽车）。 虽然强大，这种概念搜索方法的大型文档集合是不听话的，由于高复杂性的计算要求执行矩阵奇异值分解（SVD）必要的LSA。 使用子集方法的近似技术必然会引入一定量的系统误差。 为了确定一个给定的焦点文件在一个大的集合中最相关的文件，这个提出的创新（搜索子集LSA）将子集使用专有的搜索方法，没有任何系统性的错误，减少了文件的数量进行比较和分析的术语的数量，从而使实时文档相关性成为可能。 本研究的目的是：确定最佳子集的方法，全面的逻辑捕获的顶部相关的候选人，以确定最佳的输入参数的焦点查询文件，并开发一个统计测试，以确认没有系统的偏见是存在于这种方法。

This SBIR Phase I project attempts to address a fundamental question for innovators: is my idea already patented? This do-it-yourself-initially service will empower small-business enterprises with synthesized research of over 10 million patent documents within 10 minutes based on the concepts contained within the idea. Such a comprehensive, real-time, low-cost offering is currently unavailable for small-business entrepreneurs. Current search tools do not synthesize the results into an executive summary, do not allow an entire document to be entered as the search input, and do not perform real-time concept/correlation computations. This proposed innovation will enable the inventor to submit an entire document (the idea) as the search query; then, utilizing network mathematics and artificial-intelligence algorithms, this service will synthesize search results in real-time summarizing what patent documents are most related to the idea based on natural-language processing. Such a service for small-business entrepreneurs would enable them to initially ascertain the novelty of their idea and give them an on-the-go education about the natural language used in patent documents in comparison to their idea. This meta-innovation would objectively ascertain the intellectual-property merit of any proposed technology, enable small-business innovators, and foster the acceleration of innovation development in the United States.The development of latent semantic analysis (LSA) has enabled algorithm development to extract latent (or hidden) semantic structure from documents addressing two important word-sense ambiguity issues that text-matching search cannot: polysemy (single term with multiple meanings; i.e., strike as to hit [verb], to start up [verb], or to cease working [noun]) and synonymy (multiple terms with single meaning; i.e., car and automobile). Albeit robust, this concept-search approach for large document collections is not tractable due to the high-complexity computational requirements for performing matrix singular value decomposition (SVD) necessary for LSA. Approximation techniques that use subset approaches necessarily introduce some amount of systematic error. To ascertain the most relevant documents in a large collection for a given focal document, this proposed innovation (search-subset LSA) will subset using proprietary search methodologies without any systematic error, reducing both the number of documents to compare and the number of terms to analyze thereby making real-time document correlations possible. The aims of this research are: to identify the optimal subset approach for comprehensive nomological capture of top-correlation candidates, to ascertain optimal input parameters for the focal query document, and to develop a statistical test to confirm that no systematic bias is present in this approach.