SBIR Phase I: A Deep-learning-based Chatbot and Personalized Recommendations: Application to Nutrition

这项小企业创新研究（SBIR）第一阶段项目的广泛影响是促进美国公众的健康和福利。根据美国疾病控制中心（CDC）的数据，美国成年人的肥胖率已从1990年的12%上升到今天的40%以上，2016年的医疗成本估计为2600亿美元。根据美国国立卫生研究院（NIH）的数据，2014年，70%的美国成年人超重或肥胖。据《美国新闻与世界报道》报道，2013年，美国成年人每年在减肥上花费600亿美元。《美国预防医学杂志》（American Journal of Preventive Medicine） 2008年的一项研究表明，每天记录食物的人比不记录的人减掉的体重多两倍。然而，现有的饮食跟踪方法往往过于耗时，无法维持长期的减肥效果。一个个性化的人工智能（AI）聊天机器人可以让记录食物变得有趣和简单，使数百万试图减肥的美国人受益，并加深对口语对话系统的了解。这个小企业创新研究（SBIR）第一阶段项目将从几个方面推进口语对话系统领域的知识。首先，该项目指出人工智能和口语对话系统尚未应用于营养领域，从而建立了一个新的研究领域。通常，会话代理专注于事实性问题的回答或机票预订等任务，但也有机会利用大数据来学习饮食和健康之间的关系。其次，该项目将开发一个具有记忆的神经生成聊天机器人模型，展示与智能代理进行个性化会话交互的好处，智能代理可以记住会话历史和用户的个人详细信息。虽然手动编写聊天机器人响应确保了对输出的更多控制，但缺点是响应不那么有趣、多样化和灵活。这项工作提出了生成式变形金刚，以生成更逼真的、类似人类的响应和知识图，作为一种新的方法来记住每个用户的对话和饮食跟踪历史，以获得个性化的反馈。最后，这个项目提出了因果推理的应用，通常用于医学诊断，到新的，具有挑战性的任务，预测哪些食物导致的结果，如肠道症状，体重减轻，或肌肉建设。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase I project is to advance the health and welfare of the American public. Obesity among American adults has risen from 12% in 1990 to over 40% today, leading to an estimated medical cost of $260 billion in 2016, according to the Center for Disease Control (CDC). According to the National Institute for Health (NIH), 70% of American adults were overweight or obese in 2014. In 2013, American adults were spending $60 billion annually on weight loss, according to US News & World Report. A 2008 American Journal of Preventive Medicine study showed that those who kept daily food journals lost twice as much weight as those who did not. However, existing diet tracking methods are often too time-consuming for maintaining long-term weight loss. A personalized artificial intelligence (AI) chatbot could make food logging fun and easy, benefitting millions of Americans who are trying to lose weight and furthering knowledge on spoken dialogue systems.This Small Business Innovation Research (SBIR) Phase I project will advance knowledge in the field of spoken dialogue systems in several ways. First, the project establishes a new research area by noting that AI and spoken dialogue systems have yet to be applied to nutrition. Typically, conversational agents focus on factual question answering or tasks such as flight booking, but there is an opportunity to leverage big data for learning relationships between diet and health. Second, this project will develop a neural generative chatbot model with memory, demonstrating the benefit of personalized conversational interactions with intelligent agents that remember the history of conversations and personal details about the user. While manually writing chatbot responses ensures more control over the output, the drawback is that the responses are less interesting, diverse, and flexible. This work proposes generative Transformers in order to generate more realistic, human-like responses and knowledge graphs as a novel method for remembering the conversation and diet tracking history of each user for personalized feedback. Finally, this project proposes the application of causal inference, often used for medical diagnosis, to the new, challenging task of predicting which foods lead to outcomes such as gut symptoms, weight loss, or muscle building.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.