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

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响是促进美国公众的健康和福利。根据疾病控制中心（CDC）的数据，美国成年人的肥胖率从1990年的12%上升到今天的40%以上，导致2016年的医疗费用估计为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。2008年《美国预防医学杂志》的一项研究表明，那些坚持每天写饮食日记的人比那些不写日记的人减掉的体重多一倍。然而，现有的饮食跟踪方法对于维持长期减肥来说往往过于耗时。一个个性化的人工智能（AI）聊天机器人可以使食物记录变得有趣和容易，使数百万试图减肥的美国人受益，并进一步了解口语对话系统。这个小企业创新研究（SBIR）第一阶段项目将以多种方式推进口语对话系统领域的知识。首先，该项目建立了一个新的研究领域，指出人工智能和语音对话系统尚未应用于营养。通常情况下，会话代理专注于事实问题的回答或航班预订等任务，但有机会利用大数据来学习饮食与健康之间的关系。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对话交互的好处，这些智能代理会记住对话的历史和用户的个人细节。虽然手动编写聊天机器人响应可以确保对输出的更多控制，但缺点是响应不那么有趣，多样性和灵活性。这项工作提出了生成式变形金刚，以生成更逼真的，类似人类的反应和知识图，作为一种新的方法，用于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，该项目提出了因果推理的应用，通常用于医疗诊断，到新的，具有挑战性的任务，预测哪些食物会导致结果，如肠道症状，体重减轻，或肌肉建设。该奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

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.