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title: Patent ideas VMWare

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# Patent ideas VMWare

Based on the detailed insights from the document on deploying enterprise-ready generative AI on VMware, here are three strong patent ideas combining AI, ML, and solutions to significant and impactful problems faced by enterprises:

1. **AI-Driven Adaptive Resource Management System**:
   - **Problem Addressed**: Efficient resource utilization in enterprise environments, especially those involving complex AI and ML workflows.
   - **Solution**: An AI system that dynamically allocates and deallocates resources based on real-time workload demands and predictive analytics. This system would leverage machine learning models to predict upcoming resource needs and automatically adjust the infrastructure to optimize for cost, performance, and energy consumption.

The integration of artificial intelligence (AI) in resource management systems has garnered significant attention in recent years. AI-driven adaptive resource management systems have been proposed to address the challenges of real-time, distributed, and robust resource allocation strategies in various domains, including edge AI systems, network slicing management, wireless communications, and 6G networks (Letaief et al., 2022; Lei et al., 2021; Qureshi & Tekin, 2020; Yang et al., 2020). These systems leverage operation research-based theory-driven and machine learning-based data-driven methods to optimize resource allocation and enhance real-time performance (Letaief et al., 2022; Qureshi & Tekin, 2020). Additionally, AI-enabled intelligent architectures have been developed to support smart resource management, automatic network adjustment, and intelligent service provisioning, with a focus on the sensing layer, data mining and analytics layer, control layer, and application layer (Yang et al., 2020).

Furthermore, AI has been identified as a crucial component in the elastic management and orchestration of 5G and 6G networks, as well as in the deployment and management of vertical services over 5G networks Gutierrez-Estevez et al. (2019)Li et al., 2021). The use of AI in these contexts aims to automate service management, adapt to dynamic changes, optimize resources, and support intelligent management architectures (Li et al., 2021). Moreover, AI has been recognized as a valuable tool for managing water resources, disaster response and recovery efforts, and health resource distribution, particularly during the COVID-19 pandemic (Wu et al., 2023; Chang & Guo, 2020; Emami (2023)Laudanski et al., 2020).

The potential of AI in resource management extends beyond technical domains, as it has implications for human resource management, organizational capabilities, and governance. AI-driven human resource management is expected to influence job design, transparency, performance, and data ambiguity, thereby impacting sustainable company development (Böhmer & Schinnenburg, 2023). Additionally, AI-related technologies can aid policy-makers in evidence-driven, data-intensive decision-making and simulation, contributing to good governance (Margetts, 2022).

The evidence from the selected references confirms the growing significance of AI-driven adaptive resource management systems across various domains. These systems leverage AI to optimize resource allocation, automate service management, support intelligent management architectures, and enhance decision-making processes. The potential applications of AI in resource management extend to diverse areas, including wireless networks, disaster management, healthcare, and governance.

2. **Secure AI Model Training and Deployment Platform**:
   - **Problem Addressed**: Security and compliance issues surrounding the training and deployment of AI models in regulated industries.
   - **Solution**: A platform that provides end-to-end encryption and secure enclaves for training AI models, ensuring that all data and model parameters remain confidential. The platform would use ML to enhance threat detection and automate compliance processes, providing a robust solution for industries such as finance and healthcare where data sensitivity is paramount .

3. **AI-Enhanced Automation of IT Infrastructure Management**:
   - **Problem Addressed**: The complexity and manual effort required in managing IT infrastructure, particularly in large enterprises with extensive virtual environments.
   - **Solution**: An AI-driven management system that automates the monitoring, troubleshooting, and optimization of IT infrastructures. Utilizing ML algorithms, the system would predict failures and bottlenecks and autonomously execute mitigation strategies to maintain system health and performance, thus reducing downtime and operational costs .
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### References:
Böhmer, N. and Schinnenburg, H. (2023). Critical exploration of ai-driven hrm to build up organizational capabilities. Employee Relations, 45(5), 1057-1082. https://doi.org/10.1108/er-04-2022-0202
Chang, F. and Guo, S. (2020). Advances in hydrologic forecasts and water resources management. Water, 12(6), 1819. https://doi.org/10.3390/w12061819
Emami, P. (2023). The synergy of artificial intelligence (ai) and geographic information systems (gis) for enhanced disaster management: opportunities and challenges. Disaster Medicine and Public Health Preparedness, 17. https://doi.org/10.1017/dmp.2023.174
Gutierrez-Estevez, D., Gramaglia, M., Domenico, A., Dandachi, G., Khatibi, S., Tsolkas, D., … & Wang, Y. (2019). Artificial intelligence for elastic management and orchestration of 5g networks. Ieee Wireless Communications, 26(5), 134-141. https://doi.org/10.1109/mwc.2019.1800498
Laudanski, K., Shea, G., DiMeglio, M., Rastrepo, M., & Solomon, C. (2020). What can covid-19 teach us about using ai in pandemics?. Healthcare, 8(4), 527. https://doi.org/10.3390/healthcare8040527
Lei, L., Yuan, Y., Vu, T., Chatzinotas, S., Minardi, M., & Montoya, J. (2021). Dynamic-adaptive ai solutions for network slicing management in satellite-integrated b5g systems. Ieee Network, 35(6), 91-97. https://doi.org/10.1109/mnet.111.2100206
Letaief, K., Shi, Y., Lu, J., & Lu, J. (2022). Edge artificial intelligence for 6g: vision, enabling technologies, and applications. Ieee Journal on Selected Areas in Communications, 40(1), 5-36. https://doi.org/10.1109/jsac.2021.3126076
Li, X., García‐Saavedra, A., Costa‐Pérez, X., Bernardos, C., Guimarães, C., Antevski, K., … & López, D. (2021). 5growth: an end-to-end service platform for automated deployment and management of vertical services over 5g networks. Ieee Communications Magazine, 59(3), 84-90. https://doi.org/10.1109/mcom.001.2000730
Margetts, H. (2022). Rethinking ai for good governance. Daedalus, 151(2), 360-371. https://doi.org/10.1162/daed_a_01922
Qureshi, M. and Tekin, C. (2020). Fast learning for dynamic resource allocation in ai-enabled radio networks. Ieee Transactions on Cognitive Communications and Networking, 6(1), 95-110. https://doi.org/10.1109/tccn.2019.2953607
Wu, H., Lu, X., & Wang, H. (2023). The application of artificial intelligence in health care resource allocation before and during the covid-19 pandemic: scoping review. Jmir Ai, 2, e38397. https://doi.org/10.2196/38397
Yang, H., Alphones, A., Xiong, Z., Niyato, D., Zhao, J., & Wu, K. (2020). Artificial-intelligence-enabled intelligent 6g networks. Ieee Network, 34(6), 272-280. https://doi.org/10.1109/mnet.011.2000195