SBIR Phase II: Project to Create an Automated System to Identify and Curate Web-based Resources for Entrepreneurs

SBIR 第二阶段：创建一个自动化系统来为企业家识别和管理基于网络的资源的项目

• 批准号: 1431025
• 负责人: Bradley Davis
• 金额: $ 75万
• 依托单位: Enclavix, LLC
• 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: 2014-09-01 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1431025&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Project; Create

This SBIR Phase II project will develop an online system to find and organize the best web-based resources for entrepreneurs. Entrepreneurs have the opportunity to create profitable, society-changing companies with high quality jobs, but many entrepreneurs lack skills in the areas of capital, finance, legal and startup management which are required to convert an opportunity into a successful company. Many entrepreneurs fail because they "do not know what they do not know", because they do not know about available resources, or because they cannot readily find those resources. Many resources are available to entrepreneurs, but with millions of search engine links on the topic, the most useful of these are nearly impossible to find. The proposed online system will use machine intelligence software to identify and categorize a wide array of resources. It will use another machine intelligence system to grade the quality of these resources, and then use a unique display system to show the most helpful and relevant resources in a dynamic constellation-like interface. This is designed to improve the ability of entrepreneurs to explore and find the help they need to solve their problems. This project requires state of the art software development to train computers to identify and qualify each resource in order to select those most helpful to entrepreneurs. By helping entrepreneurs be more successful, more rapidly, this project will help grow America's economy, generate revenue and profits and create new jobs for businesses all across the country. The research objective of this SBIR Phase II proposal is to deliver a working system to help accelerate entrepreneurs by guiding them to the resources that will best help them achieve success. Specifically, during this project, the team will develop multiple machine intelligence systems to perform the classification, categorization and quality assessment of online resources; build on the existing Enclavix semantic network; and collaborate with the University of Utah to implement an advanced visualization system. This Phase II proposal is advancing the state of research in all three research areas. This project will include developing a machine intelligence engine to take an entrepreneur focused web-based resource and place it in the proper category in the semantic network. A second machine intelligence engine will solve the difficult problem of determining the quality of a given resource. The machine intelligence systems that are being proposed are very complex and are pushing the boundaries of this technology because of the number of resources that need to be evaluated. In addition, again because of the large number of target resources available, visualizing and exploring the data is challenging and requires an advanced visualization system that provides a new way to successfully access large amounts of data.

SBIR第二阶段项目将开发一个在线系统，为企业家寻找和组织最好的网络资源。企业家有机会创造有利可图的、改变社会的公司，提供高质量的工作，但许多企业家缺乏资本、金融、法律的和创业管理方面的技能，而这些技能是将机会转化为成功公司所必需的。许多企业家失败是因为他们“不知道他们不知道的事情”，因为他们不知道现有的资源，或者因为他们不能轻易找到这些资源。许多资源可供企业家使用，但与数百万搜索引擎链接的主题，其中最有用的几乎是不可能找到的。拟议的在线系统将使用机器智能软件来识别和分类各种资源。它将使用另一个机器智能系统来对这些资源的质量进行分级，然后使用一个独特的显示系统，以动态星座式的界面显示最有帮助和最相关的资源。这是为了提高企业家探索和寻找解决问题所需帮助的能力。该项目需要最先进的软件开发，以培训计算机识别和鉴定每种资源，以便选择对企业家最有帮助的资源。通过帮助企业家更成功，更迅速，这个项目将有助于美国经济的增长，创造收入和利润，并为全国各地的企业创造新的就业机会。SBIR第二阶段提案的研究目标是提供一个工作系统，通过引导企业家获得最能帮助他们取得成功的资源来帮助他们加速创业。具体来说，在这个项目中，该团队将开发多个机器智能系统，以执行在线资源的分类，归类和质量评估;建立在现有的Enclaim语义网络上;并与犹他州大学合作，实现一个先进的可视化系统。第二阶段的建议正在推进所有三个研究领域的研究状况。该项目将包括开发一个机器智能引擎，以企业家为中心的基于Web的资源，并将其放在语义网络中的适当类别中。第二个机器智能引擎将解决确定给定资源质量的难题。正在提出的机器智能系统非常复杂，并且由于需要评估的资源数量而正在推动这项技术的边界。此外，同样由于大量的目标资源可用，可视化和探索数据是具有挑战性的，需要一个先进的可视化系统，提供一种新的方式来成功地访问大量的数据。