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

这个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？这项最初的DIY服务将使小型企业能够在10分钟内根据该想法中包含的概念对1000多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创业者。目前的搜索工具不将结果合成执行摘要，不允许输入整个文件作为搜索输入，也不执行实时概念/关联计算。这项拟议的创新将使发明者能够提交整个文档(创意)作为搜索查询；然后，利用网络数学和人工智能算法，这项服务将基于自然语言处理实时汇总哪些专利文档与创意最相关的搜索结果。为小企业创业者提供的这种服务将使他们能够初步确定自己想法的新颖性，并就专利文件中使用的自然语言与他们的想法进行比较，为他们提供进行中的教育。潜在语义分析(LSA)的发展使算法开发能够从文档中提取潜在的(或隐藏的)语义结构，解决文本匹配搜索无法解决的两个重要的词义歧义问题：一词多义(单个词具有多个含义；例如，敲击[动词]、启动[动词]或停止工作[名词])和同义词(多个具有单一含义的术语，即汽车和汽车)。尽管这种概念搜索方法很健壮，但由于执行LSA所需的矩阵奇异值分解(SVD)的高复杂性计算要求，这种针对大型文档集合的概念搜索方法并不容易处理。使用子集方法的近似技术必然会引入一些系统误差。为了确定给定焦点文档的大集合中最相关的文档，这一拟议的创新(搜索子集LSA)将使用专有搜索方法来子集，而没有任何系统错误，从而减少了要比较的文档的数量和要分析的词语的数量，从而使得实时文档关联成为可能。本研究的目的是：确定最佳子集方法，以全面地从法理上捕捉最相关的候选者，确定焦点查询文档的最佳输入参数，并进行统计检验，以证实该方法不存在系统性偏差。

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.