SBIR Phase I: Automating Conversational Systems For Enterprise Customer Support

SBIR 第一阶段：用于企业客户支持的自动化对话系统

• 批准号: 1819808
• 负责人: Yanjia Yao
• 金额: $ 22.45万
• 依托单位: TOPBOTS Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1819808&HistoricalAwards=false
• 关键词: SBIR; Phase; Automating; Conversational; Systems

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a scalable, enterprise-ready conversational AI platform that enables businesses to automate customer support requests, optimize call routing efforts to get customers to the right human support agent, generate customer self-service resources like smart FAQs, augment human staff with bot capabilities, and enable personalized recommendations to support sales and purchasing decisions for customers. Such technology is beneficial to any enterprise organization, B2C (business-to-consumer) and B2B (business-to-business), who needs to communicate to customers, employees, and the public. With rising customer service and communication costs, automation is critical for businesses to maintain profitability and competitive advantages. On a societal level, conversational interfaces democratize access to information and resources worldwide. A significant portion of the world's population lives in regions where internet access is poor and device ownership is rare. Many also suffer from physical limitations, such as impaired vision or limited motor control. Accelerating technology access through conversational interfaces such as SMS and voice has already been shown to transform quality of life for disadvantaged groups through applications in telemedicine, personal finance, and education.This Small Business Innovation Research (SBIR) Phase I project aims to enable human-level conversational ability in machines. Teaching computers to communicate like humans is a critical step to achieving human-level machine intelligence (HLMI), also called artificial general intelligence (AGI). Current state-of-the-art approaches to automated dialog systems use a combination of supervised learning and reinforcement learning implemented with deep neural network architectures. These approaches have shown promise on toy problems in academic research, but have mixed performance in enterprise settings. This Phase 1 project extends current deep learning approaches by developing a repeatable methodology for annotating and preparing enterprise data for use in dialog systems and a scalable, grounded neural network architecture that can reference external knowledge sources with minimal manual engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将来自于一个可扩展的，企业就绪的对话式人工智能平台的开发，该平台使企业能够自动化客户支持请求，优化呼叫路由工作以将客户带到正确的人工支持代理，生成客户自助服务资源，如智能常见问题解答，增强人工智能功能，并实现个性化推荐，以支持客户的销售和购买决策。这种技术对任何需要与客户、员工和公众进行通信的企业组织、B2C（企业对消费者）和B2B（企业对企业）都是有益的。随着客户服务和通信成本的上升，自动化对于企业保持盈利能力和竞争优势至关重要。在社会层面上，对话界面使全球信息和资源的获取民主化。世界上很大一部分人口生活在互联网接入不佳和设备拥有率很低的地区。许多人还患有身体限制，如视力受损或运动控制有限。通过短信和语音等对话界面加速技术接入，已经证明可以通过远程医疗、个人理财和教育等应用，改变弱势群体的生活质量。这一小型企业创新研究（SBIR）第一阶段项目旨在实现机器的人类水平对话能力。教计算机像人类一样交流是实现人类水平的机器智能（HLMI）的关键一步，也称为人工通用智能（AGI）。目前自动对话系统的最先进方法使用监督学习和强化学习的组合，这些学习是用深度神经网络架构实现的。这些方法在学术研究中对玩具问题表现出了希望，但在企业环境中表现不一。这个第一阶段项目通过开发一种可重复的方法来扩展当前的深度学习方法，该方法用于注释和准备企业数据以用于对话系统，该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持的搜索.