Transforming Phone Call Behavioral Modeling into Innovations in Automated Action Systems for Electronic Devices

Impact summary

The research article titled "Evidence-based behavioral model for calendar schedules of individual mobile phone users," co-authored by myself, has significantly influenced the development of intelligent call management systems. Published in the 3rd IEEE International Conference on Data Science and Advanced Analytics (DSAA) in 2016, this research was cited as one of the 16 scholarly outputs in Samsung Electronics Co. Ltd.'s patent "Method for automating actions for an electronic device" (US patent number US11972327B2, granted in 2024). This citation underscores the article’s contribution to advancing automated action systems in electronic devices, particularly in the area of intelligent call interruption management.

Research and engagement activities leading to impact

Rationale: The increasing use of mobile devices necessitates intelligent systems that manage interruptions, such as incoming calls, in a contextually appropriate and personalized manner. Traditional models rely on static, non-personalized mappings between calendar events and user behaviors, which do not accurately reflect the dynamic nature of individual responses to phone calls. This research aimed to address this gap by developing a dynamic, evidence-based behavioral model (EBM) for better prediction and management of call responses.

Research Work and Engagement Activities: Co-supervising a PhD student in collaboration with two other researchers, we developed an EBM that learns and predicts individual users' call response behaviors based on mobile phone logs and calendar events. The research utilized real-world datasets to validate the model’s effectiveness in capturing nuanced user behaviors. This work produced 1 Q1 journal and 11 conference papers (CORE rank 5 A*, 1 A and 4 B), including the DSAA 2016 paper. The findings were recognized for their innovative approach and practical implications.

End-users Involved: The primary end-users of this research are technology developers, mobile device manufacturers, and software engineers who create intelligent systems for mobile devices. The citation by Samsung Electronics highlights the research’s relevance to commercial applications in the technology sector, particularly in enhancing call management features.

Relevant Inputs: The research benefited from the guidance provided during the PhD project, collaboration with the student on data analysis, and the use of extensive mobile phone log datasets for model validation. The publication in high-impact journals and prominent conferences ensured the research reached key stakeholders in the academic and technology communities.

Research outputs associated with the impact

The key outputs from this research include the publication of one Q1 journal article and 11 conference papers (CORE rank 5 A*, 1 A and 4 B), including the DSAA 2016 paper. This comprehensive body of work introduced a novel approach to behavioral modeling for mobile call management and has been recognized for its innovation and practical relevance.

Researcher involvement

This project emerged from my PhD research and represents a continuation of my work. As the second co-author and co-supervisor of the PhD student, I played a crucial role in the conceptualization, development, and validation of the behavioral model. My responsibilities included guiding the student through research methodology, data analysis, and writing the papers. This collaborative effort ensured the research addressed practical challenges in intelligent call management systems and built upon the foundations established in my doctoral studies.

Outcomes of research leading to impact

The research has been adopted by Samsung Electronics in their patented technology for automating actions in electronic devices. The citation of our paper as one of the 16 cited scholarly outputs in the patent demonstrates the influence of our work on the development of sophisticated, context-aware call management systems. This outcome highlights the practical application of our research in improving mobile device functionality.

Beneficiaries of the impact

Primary Beneficiaries: The primary beneficiaries are Samsung Electronics and other technology developers working on intelligent systems for mobile devices. By incorporating the principles of the evidence-based behavioral model (EBM), these companies can develop more personalized and effective call management features.

Secondary Beneficiaries: Mobile device users benefit from improved call management features that reduce interruptions during important events. Additionally, the broader academic and research community benefits from the insights and methodologies developed in this study, which can inform future research in intelligent systems and behavioral modeling.

Reach: The research has had a global impact, influencing product development at a major multinational corporation like Samsung Electronics. The reach extends beyond academia, demonstrating the relevance of the research in commercial products used by millions worldwide.

Details of the impact achieved

Significance: The citation of our research in Samsung’s patent underscores the importance of the evidence-based behavioral model (EBM) in advancing intelligent call management systems. The model’s dynamic and personalized approach has contributed to the development of automated systems that better manage incoming calls based on individual user behaviors and calendar events, improving user experience and productivity.

Evidence: The impact is evidenced by the citation of our work in Samsung Electronics' U.S. patent "Method for automating actions for an electronic device" (US11972327B2), where our research is listed as one of the 16 cited scholarly outputs. This citation indicates the practical application and influence of our research on the development of new technologies in the consumer electronics industry.

Impact date	2024
Category of impact	Quality of life Impact, Other Impact
Impact level	International