SBIR Phase I: An Ergosemantic Analogy Problem Solving Platform

SBIR 第一阶段：人体工学类比问题解决平台

• 批准号: 1345439
• 负责人: Anthony McCaffrey
• 金额: $ 14.94万
• 依托单位: Innovation Accelerator, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-01-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1345439&HistoricalAwards=false
• 关键词: SBIR; Phase; Ergosemantic; Analogy; Problem

This SBIR Phase I project proposes to develop a novel software platform for conducting analogous search to enhance innovation. The technology is based on the observation that as much as 90% of new innovations are really adaptations of existing solutions. Currently, engineers responsible for innovating new products generally rely on basic brainstorming tools and a hit or miss strategy. This makes innovation more art than science. Analogy Finder is designed to address this problem. It is based on patent pending semantic search technology that locates highly relevant patents containing adaptable ideas for unsolved problems. The technology, originally developed under NSF funding at the University of Massachusetts, enables engineers and scientists to move beyond keyword search and instead conduct analogous search to identify relevant technologies and solutions from other disciplines. The proposed phase I project will enhance the relevancy of analogous search results, while developing a suite of benchmark metrics to measure improvement. The objectives of the project are to implement two ways to group search results and one way to order search results by a semantic uniqueness metric, and to complete the indexing of all digitized patents of the U.S. Patent database.The broader/commercial impact of this technology will be seen by companies that are actively engaging in and investing in their R&D process to increase innovation. Obvious industries benefiting from the technology include automotive, electronics, aerospace, defense, energy, infrastructure and chemical where they rely heavily on engineering design. The new technology fulfills a distinct and crucial need for the industry. According to a 2007 McKinsey study, 70% of Corporate Leaders say innovation is among their top three priorities for driving growth, indicating a prime opportunity for our technology. But as outlined in the Booze & Allen 2012 report, few companies have perfected the innovation process. According to the survey, 46% of respondents said their companies are only marginally effective or average at generating commercially viable ideas and few succeed at innovation without having an established process that is followed in a disciplined fashion. Adding science to the art of innovation will greatly increase the discovery of new, innovative solutions for a myriad of engineering problems. When a company can more accurately predict the time needed to solve a problem it will allow them to save time, innovate faster and speed the time to market for new and innovative products.

这项SBIR第一阶段项目建议开发一个新的软件平台，用于进行类比搜索，以促进创新。这项技术是基于这样一种观察，即多达90%的新创新实际上是对现有解决方案的改编。目前，负责创新新产品的工程师通常依赖于基本的头脑风暴工具和命中或失败的策略。这使得创新更像是艺术而非科学。类比推理就是为了解决这个问题而设计的。它基于专利申请语义搜索技术，定位高度相关的专利，其中包含针对未解决问题的适应性想法。这项技术最初是在马萨诸塞州大学的NSF资助下开发的，它使工程师和科学家能够超越关键字搜索，而是进行类似搜索，以识别其他学科的相关技术和解决方案。拟议的第一阶段项目将提高类似搜索结果的相关性，同时制定一套衡量改进情况的基准指标。该项目的目标是实现两种方法来分组搜索结果和一种方法来排序搜索结果的语义唯一性度量，并完成索引的所有数字化专利的美国专利数据库。该技术的更广泛的/商业影响将被那些积极参与和投资于他们的研发过程，以增加创新的公司看到。 受益于该技术的明显行业包括汽车，电子，航空航天，国防，能源，基础设施和化学，这些行业严重依赖工程设计。新技术满足了该行业独特而关键的需求。 根据麦肯锡2007年的一项研究，70%的企业领导人表示，创新是他们推动增长的三大优先事项之一，这表明我们的技术面临着最好的机会。 但正如布兹艾伦2012年报告所概述的那样，很少有公司完善了创新过程。根据调查，46%的受访者表示，他们的公司在产生商业上可行的想法方面效率很低或一般，如果没有一个以纪律性方式遵循的既定流程，很少有公司能在创新方面取得成功。在创新的艺术中加入科学，将大大增加对无数工程问题的新的创新解决方案的发现。 当一家公司能够更准确地预测解决问题所需的时间时，它将使他们能够节省时间，更快地创新，并加快新产品和创新产品的上市时间。