Energy savings system based machine learning of wireless performance activity for mobile information handling system connected to plural wireless networks

A method of modulating performance of a plurality of wireless network interface modules for a mobile information handling system for power efficiency, comprising:

• receiving, at a processor executing instructions of a wireless utilization machine learning inference modulator, an iterative wireless utilization profile indicating aggregated time and location profiles of wireless network utilization parameters, the performance of each of the plurality wireless network interface modules, and user configuration states of the mobile information handling system for data needs;
• and
• executing code instructions of a machine learning service of the wireless utilization machine learning inference modulator to determine a predictive time and date based plural wireless control setting index for predictively adjusting the performance of the wireless network interface modules during an impending predictive time period of wireless system utilization based on information associated with the received iterative wireless utilization profiles and updating the predictive time and date based plural wireless control setting index to a wireless recommendation history database;
• accessing the predictive time and date based plural wireless control setting index correlating to a current calendar day, time of day and location pattern of the predictive time period by the wireless network interface control system;
• and
• and predictively adjusting the control settings of at least one of the plurality of wireless network interface modules.

A mobile information handling system comprising:

• a processor, a memory, a power management system, and a plurality of wireless network interface modules for wireless connection to a plurality of wireless networks;
• a wireless network interface control system for controlling the plurality of wireless network interface modules;
• the processor executing instructions of a wireless utilization machine learning inference modulator to receive time and date based parameter data factors relating to time of day, day of the week, day of the month, day of the year, and time proximity to scheduled events of a user;
• the wireless utilization machine learning inference modulator to apply a machine learning classification algorithm to match correlation of learned patterns in the time and date based parameter data factors of an impending predictive time period of wireless system utilization and select a matching previously determined predictive time and date based plural wireless control setting index for predictively adjusting the control settings of the plurality of wireless network interface modules during the impending predictive time period of wireless system utilization;
• and
• the wireless utilization machine learning inference modulator accessing the matched predictive time and date based plural wireless control setting index correlating to a current calendar day, time and location of an impeding predictive time period to suggest to the user, via a graphical user interface, predictive adjustments to modify control settings of a subset of the plurality of wireless network interface modules to reduce energy consumption.