SmartMeet: An AI-powered Meeting Scheduler with Email Automation

References

[1] J. Cranshaw et al., “Calendar help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop,” in Proc. CHI Conf. Human Factors in Computing Systems, Denver, CO, May 2017, pp. 2382–2393.

[2] S. Busemann, T. Declerck, et al., “Natural Language Di- alogue Service for Appointment Scheduling Agents,” in Proc. 5th Conf. Applied Natural Language Processing, Washington, DC, 1997, pp. 25–32.

[3] D. Vishwanath and L. Vig, “Meeting Bot: Reinforce- ment Learning for Dialogue Based Meeting Scheduling,” in Proc. AAAI Conf. Artificial Intelligence, New Orleans, LA, Feb. 2018, pp. 8135–8136.

[4] K. Yang and N. Patan, “Multi-Agent Meeting Scheduling Using Mobile Context,” in Proc. 24th Int. Conf. Intelli- gent User Interfaces, Marina del Ray, CA, Mar. 2019, pp. 423–434.

[5] E. Shalakhs, H. Koo, and D. Benoit, “A Distributed Multi-Agent Meeting Scheduler,” J. Computer Science and Technology, vol. 18, no. 3, pp. 291–304, May 2003.

[6] S. G. Goyal and D. A. Sood, “Learning to Sched- ule Meetings with a Calendar Virtual Assistant,” arXiv:2109.04596, Sept. 2021.

[7] M. G. Lee et al., “TaskMining: A Task- Centric Meeting Assistant,” Proc. ACM on Human-Computer Interaction, vol. 4, no. CSCW, pp. 1–26, Oct. 2020.

[8] Y. K. Thang et al., “A Context-Aware Personal Assistant for Managing Social Events,” in 24th Int. Conf. Intelli- gent User Interfaces, Marina del Ray, CA, Mar. 2019, pp. 423–434.

[9] B. A. Plummer et al., “Dialogue Learning with Human- in-the-Loop,” in Proc. 34th Int. Conf. Machine Learning, vol. 70, Sydney, Australia, Aug. 2017, pp. 70–81.

[10] A. Rastogi et al., “Schema-Guided Dialogue State Track- ing for Virtual Assistant,” in Proc. 21st Annual SIGdial Meeting on Discourse and Dialogue, Virtual, July pp. 1– 11.

[11] L. P. Willcocks, M. Lacity, and A. Craig, “The IT Func- tion and Robotic Process Automation,” LSE Outsourcing Unit Working Paper Series, Rep. 15/05, 2015.

[12] A. Vaswani et al., “Attention Is All You Need,” in Ad- vances in Neural Information Processing Systems 30, Long Beach, CA, Dec. 2017, pp. 5998–6008.

[13] T. B. Brown et al., “Language Models are Few-Shot Learners,” in Advances in Neural Information Process- ing Systems 33, Virtual, Dec. 2020, pp. 1877–1901.

[14] A. Radford et al., “Robust Speech Recognition via Large- Scale Weak Supervision,” arXiv:2212.04356, Dec. 2022.

[15] D. Jurafsky and J. H. Martin, Speech and Language Pro- cessing, 3rd ed. Stanford, CA: Stanford University, 2023.

[16] D. Xiao, M. Chen, and J. Zhang, “Enterprise Calendar Integration Patterns Using RESTful APIs,” IEEE Trans. Services Computing, vol. 12, no. 4, pp. 654–667, July- Aug. 2019.

[17] A. Cambria and B. White, “Jumping NLP Curves: A Re- view of Natural Language Processing Research,” IEEE Computational Intelligence Magazine, vol. 9, no. 2, pp. 48– 57, May 2014.

[18] M. Henderson, B. Thomson, and S. Young, “Deep Neu- ral Network Approach to Dialogue State Tracking Chal- lenge,” in Proc. SIGDIAL 2013 Conf., Metz, France, Aug. 2013, pp. 467–471.

[19] S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Hoboken, NJ: Pearson, 2020.

[20] P. Lison and C. Kennington, “OpenDial: A Toolkit for Developing Spoken Dialogue Systems for Probabilis- tic Rules,” in Proc. ACL 2016 System Demonstrations, Berlin, Germany, Aug. 2016, pp. 67–72.

@article{1717493,
      author = {Shiwam Maddhesiya, Rohan Prasad, Ali Abdullah, Rahul Yadav, Harvendra Kumar Patel},
      title = {SmartMeet: An AI-powered Meeting Scheduler with Email Automation},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {950-958},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717493.pdf},
      abstract = {Despite being key components of modern-day organizational work flows, coordinating meetings currently requires a con- siderable amount of manual effort, time, and is error-prone. Current solutions typically use either semi-automatic schedul- ing that requires a structure of user input or partially manual scheduling that relies on some degree of human involvement. As such, we propose SmartMeet - an AI system designed for fully automatic meeting coordination based on natural lan- guage commands from its users. SmartMeet leverages several advanced technologies for speech and natural language processing to enable full auton- omy of the system. Among those technologies include au- tomatic speech recognition, natural language understanding via transformers, large language model reasoning, and back- end automation systems. In essence, with SmartMeet users can schedule their meetings through either spoken or written speech and have the system extract all necessary details, find a suitable time period, make corresponding calendar entries, generate meeting URLs, and send automated emails without any human assistance. Based on prior work, the current version of SmartMeet was further developed to improve usability, enhance conflict reso- lution mechanisms, and increase overall integration. Contrary to conventional approaches to scheduling, SmartMeet works using a speech-first method and requires no additional man- ual coordination. Experimentally, it is shown that SmartMeet considerably decreases the scheduling time while minimizing booking errors and improving productivity of the organization in question.},
      keywords = {Automated Meeting Coordination, Natural Language Understanding, Large Language Models, Calendar Integration, Email Automation, Conflict Resolution, Intelli- gent Scheduling},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717493}
  }